International Journal of Automation and Control (57 papers in press)

Regular Issues

• Synchronisation of Chaotic Systems using Neural Generalized Predictive Control  
  by Zakaria Driss, Noura Mansouri 
  Abstract: In this paper, a successful implementation of a Neural Generalized Predictive Control (NGPC) method for synchronisation of uncertain chaotic and hyperchaotic systems is presented. For this purpose, multi-layer feedforward neural network and particle swarm optimization method (PSO) are used as system's model and optimization algorithm, respectively. The synchronisation of two 3D Lorenz systems and 4D L"{u} hyperchaotic systems is investigated using the proposed method in different situations: complete synchronisation, hybrid synchronisation, and synchronisation based on one control input. Simulation results show satisfying performance of the proposed implementation in terms of the quality of the control input and the ability to solve many problems with only slight adaptations.
  Keywords: chaos theory; synchronisation; generalized predictive control; neural network; particle swarm optimization.
  
  
• Inverse Plant Model and Frequency Loop Shaping based PID Controller design for Processes with Time-Delay  
  by Sudipta Chakraborty, Asim K. Naskar, Sandip Ghosh 
  Abstract: To achieve satisfactory set-point tracking and load disturbance rejection, two approaches for PID controller design is presented in this paper. One is based on Internal Model Control (IMC) and another is based on frequency loop-shaping. IMC is an extensively adopted strategy in process industries. This work puts a new light on IMC based controller synthesis for processes with time-delay. A new PI and PID controller synthesis methods are presented for the processes without having integrator or slow pole and the explicit formulas for controller parameters are derived in terms of the inverse plant model. But, with a PD type controller, conventional IMC techniques fail to provide a satisfactory regulatory response for integrating processes and use of an integral action may lead to a large overshoot in servo response. To address this issue, a modified IMC structure with a second compensation for integrating processes is proposed to achieve desired servo as well as regulatory responses. Next, a frequency loop-shaping based design is proposed and the guidelines for choosing the desired loop-shape are also presented. To obtain the controller parameters in frequency loop-shaping framework, the optimization problem is solved with primal-dual path following interior point method. To demonstrate the effectiveness of the proposed controllers, simulation comparisons with some recently developed methods are included. Moreover, the proposed method is experimentally validated on a temperature control process.
  Keywords: IMC; Integrating Processes; Time-delay; Inverse plant model; Frequency loop-shaping.
  
  
• Novel IMC filter design based PID controller design for Systems with One Right Half Plane (RHP) Pole and Dead-time  
  by K. Ghousiya Begum, A. Seshagiri Rao, T.K. Radhakrishnan 
  Abstract: In this article, design of PID controller using a modified internal model control (IMC) filter for right half plane (RHP) pole process with dead time, is proposed. To possess H2 optimal behavior, the derived IMC controller minimizes the integral square error (ISE) for step input disturbancesby defining the Blaschke product of unstable poles of the specific input and the model. Then it is converted into a single feedback loop controller as either PID or PID with first order filter on the basis of proposed underdamped IMC filter to improve the integral action and thereby providing fast response which is not feasible with critically damped filter. Maclaurin series approximation is used to design PID controller and Pades approximation is used to design PID with first order lead-lag filter. Various first order plus dead time (FOPDT) examples are taken and simulation is executed on diverse unstable processes and compared with some of the developed methods in recent time in the literature. The two proposed controllers provide significant improvement with respect to both nominal and perturbed conditions. The robustness studies have also been carried out for uncertainties in the plant dynamics and it is apparent that the proposed tuning method is highly robust.
  Keywords: unstable process; IMC control; H2 minimization; lead lag filter.
  
  
• Design of backstepping LQG controller for blood glucose regulation in type I diabetes patient  
  by Akshaya Kumar Patra, Pravat Kumar Rout 
  Abstract: The mechanization of the insulin infusion regulation through the artificial pancreas (AP) is needed to design with an objective to control the blood glucose (BG) level in type 1 diabetes mellitus (T1DM) patients. However, to make it applicable in real time, major components needed are availability of proper sensor augmented pumps, glucose monitoring systems, and control techniques. Recent times many researchers suggest robust control techniques for designing a robust controller for computing the required insulin dose for a highly nonlinear human metabolism system. This paper proposes a simulation model of glucose metabolism process and design of a backstepping linear quadratic Gaussian controller (BLQGC) to control the BG level in TIDM patients. In this control strategy, the basic linear quadratic regulator (LQR) is re-formulated with a state estimator based on the backstepping control approach to enhance the control performance. For designing of the BLQGC, a 9th order state-space model of the TIDM patient with micro-insulin dispenser (MID) is considered. The justification of enhanced control performance of BLQGC is demonstrated by comparative result analysis with pre-published control techniques. The simulations are carried out through MATLAB/SIMULINK environment and the results indicate comparatively better control ability of the proposed algorithm to control the BG concentration within the range of normoglycaemia in terms of accuracy, stability, quick damping and robustness.
  Keywords: type I diabetes; AP; MID; LQR; backstepping control.
  
  
• Performance Optimization for Closed Loop Control Strategies towards Simplified Model of a PMSM Drive by Comparing with Different Classical and Fuzzy Intelligent Controllers  
  by Chiranjit Sain 
  Abstract: In this proposed work a substantial comparative performance optimization has been established between the PI, Lead, Lead-Lag and fuzzy logic controllers towards the closed loop control strategies of a simplified permanent magnet synchronous motor (PMSM) drive. By the introduction of sinusoidal pulse width modulation (PWM) control strategy it is expected that the nature of armature current would be nearly sinusoidal and generated torque ripples will be lesser. In this proposed structure of a PMSM drive the speed reference has been incorporated with a speed controller to fortify that the exact speed of the proposed motor match with the base speed with null speed error. The overall structure of the PMSM drive is separated into two loop control structure, inner current loop and outer speed loop. All the necessary performance indices of the proposed PMSM drive system are tested in a MATLAB/SIMULINK environment. Moreover the performance of a fuzzy logic speed controlled PMSM drive as compared to all classical controllers provides better dynamic as well as steady state performance with reduced torque ripples. Therefore the entire performance of the proposed simplified PMSM drive in closed loop control strategy is executed and efficacy of controllers is resolved under various operating conditions. Hence the superiority of intelligent speed controller (fuzzy logic controller) for this proposed PMSM drive model over all classical controllers is validated and optimized for high performance applications. Finally an auto-tuning control strategy for the fuzzy intelligent speed controller is also proposed for optimal operation of the drive system
  Keywords: Fuzzy logic controller; Lead compensator; Lead-Lag compensator; Permanent Magnet Synchronous Motor; Voltage Source Inverter.
  DOI: 10.1504/IJAAC.2020.10020855
   
  
• Online Sensor Performance Monitoring and Fault Detection for Discrete Linear Parameter Varying Systems  
  by AQEEL MADHAG, Guoming Zhu 
  Abstract: Control system performance is heavily dependent on the sensor signals used for feedback control; and therefore, sensor performance and fault diagnostics are critical. A faulty sensor may lead to degraded system performance, system instability, or even a fatal accident. This paper proposes a fault detection (identification) algorithm to identify online sensor performance degradation and failure, where the sensor faults are characterized by variations of the sensor measurement noise covariance matrix. That is, the proposed algorithm estimates the slowly-varying sensor measurement noise covariance and detects the abrupt and/or intermittent change of sensor measurement noise covariance. To be specific, the proposed algorithm has two key features: online estimating the slowly-varying sensor measurement noise covariance and detecting the sudden (fast) change of the sensor measurement noise covariance. The covariance-matching technique, along with the adaptive Kalman filter, is utilized based on the information about the quality of the weighted innovation sequence to estimate the slowly-varying sensor measurement noise covariance. The covariance-matching of the weighted innovation sequence improves the prediction accuracy and reduces the computational load, making it suitable for real-time applications. A memory-based technique, calculating the Euclidean distance of estimated covariance matrices between two sliding estimation windows, is used to detect the abrupt (or intermittent) change of sensor noise covariance matrix. The memory-based technique is adopted due to its simplicity and online applicability. The proposed algorithm originally is designed for discrete linear time-varying (DLTV) systems and applied to discrete linear parameter-varying (DLPV) systems. Simulation results show that the proposed algorithm is capable of estimating the slowly-varying sensor measurement noise covariance and detecting the abrupt (or intermittent) change of sensor measurement noise covariance for multiple-input and multiple-output discrete linear parameter-varying systems, where the scheduling parameters lie within a compact set. Furthermore, the proposed estimation algorithm shows a reasonable rate of convergence.
  Keywords: Sensor Fault; Fault Tolerant Control; Fault Estimation; Sensor Noise Characteristics Estimation; System Monitoring; Linear Parameter Varying (LPV) System; System Catastrophic Failure; discrete linear time-varying (DLTV) systems; discrete linear parameter-varying (DLPV) systems.
  
  
• Design and analysis of novel Chebyshev neural adaptive back stepping controller for Boost converter fed PMDC motor  
  by Arunprasad Govindharaj, Anitha Mariappan 
  Abstract: An Adaptive Back stepping Chebyshev Neural Network Controller (ABCNNC) is proposed for the boost converter fed PMDC motor to track the angular velocity. The computational complexity of the neural network is avoided by the use of Chebyshev polynomials as the basis function. The online weight update of the Chebyshev Neural Network (CNN) is designed for the closed loop system based on the Lyapunov stability analysis to obtain the asymptotically stable system. A detailed analysis of the steady state and transient performance is performed and results are compared with that of conventional PI controller and Radial Basis Function Neural Network Controller (RBFNNC). To ensure the robustness of the proposed ABCNNC, it is being analyzed for a wide range of variations in load torque and the set point changes and it is validated by comparing with the conventional PI control approach and RBFNNC. Comparison of results validates that the proposed ABCNNC shows the enhanced transient and steady state responses for the uncertainties caused by disturbances, than conventional PI controller and RBFNNC.
  Keywords: Boost converter; Chebyshev Neural Network (CNN); Adaptive Back stepping Chebyshev Neural Network Controller (ABCNNC); Lyapunov stability; Chebyshev polynomials.
  
  
• Performance optimization of discrete time linear active disturbance rejection control approach  
  by Congzhi Huang, Bin Du, Chaomin Luo 
  Abstract: In the framework of the linear active disturbance rejection control (LADRC) approach, all the uncertainties, including the perturbed internal model parameters and time-varying external disturbances, can be estimated by constructing an extended state observer, and then cancelled in real time. However, the parameter tuning of the approach is an extremely challenging mission. In this paper, the bacteria foraging optimization (BFO) algorithm, and the particle swarm optimization (PSO) algorithm are proposed to optimize the performance of the system driven by the LADRC approach in light of the identified model of the servo motor. Extensive simulation results and experimental tests are given to demonstrate the proposed approaches are effective and efficient for the performance optimization of the LADRC approach.
  Keywords: algebraic parameter identification; bacteria foraging optimization; discrete time; linear active disturbance rejection control; particle swarm optimization; performance optimization.
  
  
• Simultaneous Actuator and Sensor Faults Estimation Design for LPV Systems using Adaptive Sliding Mode Observers  
  by Ali BENBRAHIM, Slim Dhahri, Faycal BENHMIDA, Anis Sellami 
  Abstract: Abstract: This paper considers the problem of simultaneous actuator and sensor faults estimation for linear parameter varying system expressed in the polytopic representation based on robust adaptive sliding mode observer technique. We start by constructing two polytopic sub-systems using transformed coordinate system design. The first sub-system includes only actuator faults. Hence, the second one is potentially faulty sensor and it is free from actuator faults. Assuming that sensor faults distribution matrix verifies the observer matching condition, we propose to conceive two adaptive sliding mode observers for estimating system states, as well as actuator and sensor faults in the presence of external disturbances. Anyway, in practise, the so-called observer matching condition is usually hard to satisfy due to the complexity and unpredictability of the system faults. Subject to relaxing this conservative condition, a new simultaneous fault estimation scheme is investigated by introducing an adaptive sliding mode observer with intermediate variable in order to estimate sensor faults. In formalism of linear matrix inequalities optimization methods, sufficient conditions are developed with H ∞ optimal performances to guarantee the stability of the proposed observers. Finally, simulation results on VTOL Aircraft defense system are highlighted to illustrate the effectiveness of the proposed simultaneous fault estimation.
  Keywords: Fault estimation; Linear parameter varying systems; Adaptive sliding mode observer; Observer matching condition.
  
  
• Adaptive Neural Network Based Robust H Tracking Control of a Quadrotor UAV under Wind Disturbances  
  by ZAKARIA BELLAHCENE, Mohamed Bouhamida, Mouloud Denai, Khaled Assali 
  Abstract: The paper deals with the stabilization and trajectory tracking control of an autonomous quadrotor helicopter system in the presence of wind disturbances. The proposed adaptive tracking controller uses Radial Basis Function neural networks (RBF NNs) to approximate unknown nonlinear functions in the system. Two controllers are proposed in this paper to handle the modeling errors and external disturbances: H_∞ adaptive neural controller H_∞-ANC and H_∞ based adaptive neural sliding mode controller H_∞-ANSMC. The design approach combines the robustness of sliding mode control (SMC) with the ability of H_∞ to deal with parameter uncertainties and bounded disturbances. Furthermore, in the RBF model, are derived using Lyapunov stability analysis. The simulation results show that H_∞-ANSMC is able to eliminate the chattering phenomena and reject mismatched perturbations and leads to a better performance than H_∞-ANC. A comparative simulation study between proposed controllers is presented and the results are discussed.
  Keywords: adaptive tracking; H_∞ control; quadrotor control; neural networks; sliding mode control.
  
  
• Large-scale global optimization using cooperative coevolution with self-adaptive differential grouping  
  by Wei Fang 
  Abstract: Cooperative co-evolution (CC) is a popular evolutionary computation approach that can divide a large problem into a set of smaller sub-problems and solve them independently. CC has been an important divide-and-conquer algorithm for large-scale global optimization (LSGO) problems. Identification of variable interactions is the main challenge in CC to decompose the LSGO problems. Differential Grouping (DG) is a competitive variable grouping algorithm that can address the non-separable components of a continuous problem. As an improved version of DG, Global Differential Grouping (GDG) addresses the drawbacks of DG which are variables interactions missing during grouping and grouping performance sensitive to the threshold. In this paper, a Self-adaptive Differential Grouping (SDG) based on GDG is proposed in order to further improve the grouping accuracy on the CEC'2010 LSGO benchmark suite. The threshold for grouping in SDG can adjust adaptively along with the magnitude of different functions and is determined by only two points which is a randomly sampled point and its corresponding opposite point in the decision space. A self-adaptive pyramid allocation (SPA) strategy that can allocate different computational resource to subcomponents is also studied in this paper. The proposed algorithm, where SDG and SPA working with the optimizer $SaNSDE$ (CCSPA-SDG), is used to optimize the CEC'2010 LSGO benchmark suite. Experimental results show that SDG achieved ideal decomposition of the variables for all the CEC'2010 LSGO benchmark functions. The optimization performance of CCSPA-SDG also outperforms the state-of-the-art results.
  Keywords: large-scale global optimization; differential grouping; cooperative co-evolution; problem decomposition.
  
  
• On-Line Closed Loop System Identification for the actuator of a flying vehicle  
  by A.N. Ouda 
  Abstract: The frequent minor wars have brought to the fore guided missiles as the main weapon against all types of military targets. Any weapon should have as high a single shot kill probability as possible. The necessity for guided weapons is motivated by reasons including the random dispersion at launch, deflection of the flight path, and the target movement or maneuvers. The identification and/or measurement of the subsystems parameters involved in these weapons are the cornerstone for any development and upgrading. One way of restoring the single shot kill probability is to use large warhead with the large lethal area, but this will usually mean a larger missile. The other method that can be adopted is to design a robust guidance system to reduce the miss-distance with high single shot kill probability. This can be accomplished by monitoring continuously the flight parameters of the missile and target, then employing this information to control the missile in space. In order to enhance the performance of an anti-tank guided missile, the selection of the nominal model is required to design the guidance computer for the system. In addition, an on line closed-loop system identification is required to adaptive autopilot design. This paper is devoted to the direct and indirect methods in system identification and using system identification in Position Control Robotic Benchmark via open loop and closed loops system identification. Hence, applying the closed loop methods to identify the actuator and airframe of the intended plant in order to design a self-tuning controller. The online system identification is based on the recursive least square method.
  Keywords: command guidance systems; CLOS; Self-Tuning; System Identification.
  
  
• Autonomous PSO-DVSF2: an optimised force field mobile robot motion planning approach for unknown dynamic environments  
  by Ziadi Safa, Mohamed Njeh, Mohamed Chtourou 
  Abstract: PSO-DVSF2 (Ziadi et al., 2016) is a PSO optimised mobile robot motion planning strategy based on the force field approach. PSO-DVSF2 has been previously developed to optimally guide the mobile robot in known dynamic environments. Autonomous PSO-DVSF2, the subject of this paper, is an improvement of PSO-DVSF2 to deal with unknown dynamic environments. In this new real-time PSO optimised mobile robot motion planning approach, the robot has to update F2 parameters all along the trajectory and not once in the beginning of the navigation as has been the case with the previous version. A comparison with the autonomous PSO-CF2 has been applied in various unknown environments (static and dynamic) using the 3D virtual Webots simulator. The robot localisation based on sensor readings with a local motion planning, and the variation of angular and linear speeds ensure the robot collision-free motion. Simulation results prove the efficiency of the autonomous PSO-DVSF2 to guide the robot along the shortest and safest path in complex unknown dynamic environments.
  Keywords: mobile robot motion planning; unknown dynamic environment; particle swarm optimisation; PSO; F2; Webots simulator.
  
  
• Design and Implementation of a Non-linear Controller and Observer for inverter fed Permanent Magnet Synchronous Motor drive using dSPACE DS1103 Controller Board  
  by Ramana Pilla, G.T. Chandrasekhar, Alice Mary Karlapudy 
  Abstract: This paper proposes a control system for the speed control of Permanent Magnet Synchronous Motor (PMSM) drive through hardware implementation using dSPACE DS1103 controller board. The proposed control scheme consists of two loops such as the inner current loop and outer speed loop. In the inner current loop a non-linear full order observer (NFOO) along with the state feedback controller (SFC) is used; whereas in outer speed loop PI controller is used as speed controller. The proposed NFOO estimates all the states of PMSM and fed back to the SFC. A non-linear controller (NLC) is designed along with SFC, to cancel out the system non-linearity using the concept of exact feedback linearization. Also the pole placement technique is implemented in order to shift all the poles to left half of the s-plane resulting the system stable in spite of uncertainties and parameter imperfections. The speed and position information of the PMSM is obtained using an encoder. The proposed control scheme has been extensively simulated under various conditions and verified experimentally through dSPACE DS1103 controller.
  Keywords: Permanent Magnet Synchronous Motor (PMSM); State feedback controller; dSPACE DS1103 controller; Pole placement; Non-linear full order observer; Exact feedback linearization; Non-linear controller.
  
  
• Assessment of Reading Material using Sensor Data  
  by Aniruddha Sinha, Sanjoy Kumar Saha, Anupam Basu 
  Abstract: Reading is characterized by a sequence of complex processing in the brain. The experienced cognitive load and engagement level play a major role in the assimilation of content. In this paper, for the evaluation of a textual content, we use Electroencephalogram (EEG) for brain signals and eyetracker for the eyegaze data. Experiment is done with two types (easy and difficult) of textual contents which are benchmarked using standard parameters of natural language processing. Features on Cognitive load and Engagement Index extracted from alpha and beta frequency bands of EEG are found to be discriminative from left-temporal and right-prefrontal lobes respectively. Statistical features, derived from the shift in eyegaze fixations from the current line to the adjacent lines, are found to be discriminative. A difficulty score is computed using a novel mapping function derived from the mixture of two partial sigmoid. This enables more objective comparison of two contents and helps in finding differences in individual reading skills.
  Keywords: textual content; eye-gaze; EEG; cognitive load; sigmoid.
  
  
• Observer-based Controller Design for Linear Time-varying Delay Systems using a New Lyapunov-Krasovskii Functional  
  by Venkatesh Modala, Sourav Patra, Goshaidas Ray 
  Abstract: This paper presents an observer-based controller design method for linear systems with interval time-varying state-delay. In this work, using a new Lyapunov-Krasovskii (LK) functional, a less conservative stabilization criterion is derived. The Wirtinger's inequality and reciprocally convex combination lemma are exploited to develop a computationally tractable method by representing the design constraints in linear matrix inequality (LMI) framework. Numerical examples are given to demonstrate the superiority of the proposed results over the existing method.
  Keywords: Time-delay system; Observer-based stabilization; Lyapunov-Krasovskii functional; Wirtinger's inequality; Reciprocally convex combination lemma; Linear Matrix inequality.
  
  
• Detection and Identification of Ascaris Lumbricoides and Necator Americanus Eggs in Microscopic Images of Fecal Samples of Pigs  
  by Kaushik Ray, Sarat Saharia, Nityananda Sarma 
  Abstract: Parasite egg detection and identification is an important task for diagnosis of many diseases. Manual identification of various types of parasite eggs is time consuming and prone to human error. It needs trained personals to effectively detect whether an object is parasite egg or not as well as it's type. Automation of the detection and identification process can help in reducing time, human effort and error rate significantly. In this paper, we proposed a system for detection and identification of parasite eggs from microscopic images of fecal samples of pigs using image processing and machine learning techniques. We considered images of two types of parasite eggs: Ascaris Lumbricoides and Necator Americanus that are commonly found in pigs. The system segments different candidate objects from the microscopic images and identify each of them as either a parasite egg of specified type or non-egg object. Identification of the segmented objects is done by Convolutional Neural Network (CNN) models that automatically extract relevant features and classify them into three classes: Ascaris egg, Necator eggs and Non-Egg object. The proposed system also performs quantification of the parasite eggs found in every input image.
  Keywords: Parasite egg; Ascaris; Necator; Non-egg; CNN; detection; identification; fecal sample; microscopic image.
  
  
• Improved robust stability and stabilization conditions for discrete-time linear systems with time-varying delay  
  by Venkatesh Modala, Sourav Patra, Goshaidas Ray 
  Abstract: This paper presents robust stability and stabilization problems ofrnlinear discrete-time systems with interval time-varying state delay. By invoking a new Lyapunov-Krasovskii functional, a less conservative delay-dependent robust stability criterion is derived in terms of linear matrix inequalities (LMIs) using the summation inequality in combination with the extended reciprocally convex inequality. Then, a delay-dependent stabilization problem of discrete-time systems is explored by designing a state feedback controller. The superiority of the proposed result over existing ones is demonstrated through numerical examples.
  Keywords: Time-delay systems; Robust stability; Stabilization; Lyapunov-Krasovskii functional; Summation inequality; Extended reciprocally convex inequality; Linear matrix inequality.
  
  
• Tuning of Extended Kalman Filter using Grey Wolf Optimization for Speed Control of Permanent Magnet Synchronous Motor Drive  
  by Ramana Pilla, Tulasichandra Sekhar Gorripotu, Ahmad Taher Azar 
  Abstract: This paper deals with tuning of Extended Kalman Filter (EKF) using Grey Wolf Optimization (GWO) for sensor less speed control of Permanent Magnet Synchronous Motor (PMSM) drive. A real-coded GWO is used to optimize the noise covariance matrices of EKF in off-line manner. The optimized values of these matrices are injected into the filter, thereby ensuring filter stability and accuracy in the estimation of rotor speed, position and machine states. The estimated speed from EKF is fed back to the speed controller and controller gains Kp and Ki are again tuned using GWO algorithm. The state and measurement covariance matrices improve the convergence of estimation process and quality of the estimated states. The simulation results show the superior performance of the proposed method when compared to Particle Swarm Optimization (PSO) method.
  Keywords: Extended Kalman Filter (EKF); Grey Wolf Optimization (GWO); Particle Swarm Optimization (PSO); Permanent magnet synchronous motor (PMSM); PI controller.
  
  
• Online Map Fusion System Based on Sparse Point-cloud  
  by Shiqin Sun, Benlian Xu 
  Abstract: With the gradual maturity of single-robot Simultaneous Localization and Mapping (SLAM) technology, the idea of using a robotic team to perform this task has attracted more and more attention. In this paper, we proposed an online map fusion strategy for a centralized architecture, in which a two-robot map building system employs multiple independent robots to work as agents and each being able to explore the environment independently through its own SLAM algorithm with a camera sensor and a central control module. When implementing fusion, the local map information is packaged and sent to the server. The server is responsible for map fusion, optimization and returning the global map to each agent. This makes each agent incorporate observations of others timely in its own SLAM running. Under the proposed framework, a vision-based SLAM algorithm is employed and tested, and the results verify that the strategy is suitable for multi-robot scenarios.
  Keywords: SLAM; map building; robot navigation; online map fusion.
  
  
• Fractional-Order Multi-Model Predictive Control for Nonlinear Processes  
  by Imen Deghboudj, Samir Ladaci 
  Abstract: This paper proposes a novel Fractional-Order Multi Model Predictive Control (FO-MMPC) design to deal with a class of nonlinear fractional order systems. Based on the assumption that the plant is governed by a fractional order nonlinear dynamical model, we are able for each operating region, to determine the linear portion of the nonlinear process as a single fractional order pole transfer function. The predictive model is then approximated by rational transfer functions using the singularity function approach in the frequency domain. The main contribution in this control approach is the use of a switching algorithm between fractional order prediction models that are approximating the nonlinear system dynamics for different operating points and input range intervals. By using the FO-MMPC for controlling the level of a conical tank system with nonlinear dynamics using a multiple fractional-order predictive models it is shown by means of numerical simulations the effectiveness of the proposed control strategy.
  Keywords: Model Predictive Control; fractional-order system; multi model system; Nonlinear system; Singularity function approximation; conical tank system.
  
  
• Vertical Emission Reduction in a Green Supply Chain and Government Subsidy Incentive Decision under Channel Preference  
  by Lihui Hu, Qian Yin, Yan Pang 
  Abstract: With the increasing awareness of social environmental protection, enterprises, society and government are paying more attention to the use of green supply chains in manufacturing industries. This paper combines green development energy conservation and emission reduction with government subsidies to build a three-stage game model including the manufacturer, retailer, and government. Considering the different preferences in dual channels, i.e., manufacturers on-line direct marketing channels and traditional retailers sales channels, this paper studies whether government should subsidise the supply side or the demand side, and the difference in subsidies under the leadership of different entities in a supply chain. The research shows that sales price of traditional retailers, wholesale price of manufacturers and demand of traditional retailers increased with the rise of preference of traditional channels. The direct selling channel price, wholesale price, and traditional retail price at the subsidised supply end are lower than those at the demand end, and the direct selling channel price is not subject to changes of leadership in the supply chain. Social welfare reaches its peak under Nash equilibrium, and the Stackelberg game model with manufacturer or retailer as the leader can ensure the largest profit. Finally, the validity of the conclusion further verified by an example analysis, which provides a certain theoretical basis from which government may formulate subsidy policies.
  Keywords: Channel preference; Stackelberg game; Government subsidies; Nash equilibrium.
  
  
• Artificial chemical reaction optimization of recurrent functional link neural networks for efficient modeling and forecasting of financial time series  
  by Sarat Nayak 
  Abstract: Contrast to multilayer neural networks, functional link artificial neural network uses functional expansion units for transferring lower input space to higher dimensions. It achieves enhanced discrimination capability through generating hyper planes in the input space. The feedback properties of recurrent networks made them more proficient and dynamic to model nonlinear systems accurately. This paper develops a recurrent functional link artificial neural network (RFLN) based forecasting model where the optimal model parameters are efficiently searched with artificial chemical reaction optimization (ACRO). The reason behind using ACRO is its faster convergence toward optimal solution with less number of tuning parameters. The optimal model is achieved through the process of artificial chemical reaction of potential RFLN structures, therefore termed as ACRRFLN. Also, three other optimization techniques, i.e. particle swarm optimization (PSO), teaching learning based optimization (TLBO), and genetic algorithm (GA) are employed to train RFLN separately. All the models are experimented and validated on forecasting closing indices of six stock markets. Results from extensive simulations clearly reveal the outperformance of ACRRFLN over other models similarly trained. Further, results from Deibold-Mariano test supported the statistical significance of the proposed model.
  Keywords: recurrent neural network; artificial chemical reaction optimization; stock market prediction; recurrent functional link neural network; particle swarm optimization; genetic algorithm; financial time series forecasting.
  
  
• Robust Power Conditioning System Based on LCL-Type Quasi-Y-Source Inverter for Grid Connection of Photovoltaic Arrays  
  by Navid Rasekh, Majid Hosseinpour, Abdolmajid Dejamkhooy, Adel Akbarimajd 
  Abstract: Design of a power conditioning system is a matter of concern in LCL-type grid-connected systems. The governing standard on power delivery has limited the Total Harmonic Distortion (THD) of the injected power into the grid. Both stability of the system and the power quality should be considered in the control of grid-tied systems. In this study, a grid-connected system consists of photovoltaic arrays, inverter, and LCL filter has been considered for investigation. The proportional resonant (PR) controller is applied in the control system. A systematic procedure has been proposed for tuning the PR controller in converter side current control (CSCC) of the grid-tied inverter. The proposed control scheme for PV-based power conditioning system aims at suppressing contents of injected current harmonics, enhancing the power quality, and ensuring the system stability. Besides, the robustness of the proposed power conditioning system against grid voltage and grid impedance variation is investigated. Simulations are carried out in MATLAB/Simulink environment to verify the credibility of the proposed approach.
  Keywords: Quasi-Y-Source Inverter; Converter side current control; PR controller; LCL filter.
  
  
• PID Self-tuning Method Based on Deep Belief Network and Improved Firefly Algorithm  
  by Lingzhi Yi, Xiu Xu, Mao Tan, Zongguang Zhang, Weihong Xiao, Lv Fan 
  Abstract: In order to overcome the difficulty of tuning the proportion integration differentiation (PID) parameters, a PID parameter self-tuning method based on the firefly algorithm improved by Newtons law of universal gravitation (LOGFA) and deep belief network (DBN) is proposed. Compared with the FA, LOGFA can not only maintain the evolutionary advantage of the original algorithm but also can effectively improve the accuracy and convergence ability of the algorithm. The advantage of DBN is to train each layer of neural network separately, which greatly improves the training efficiency and accuracy. The closed-loop PID speed control system of a three-phase asynchronous motor is used as the simulation object for PID parameter self-tuning. The proposed LOGFA-DBN is compared with other three algorithms. Simulation results show that the algorithm combining LOGFA and DBN can realize the off-line parameter tuning which is not subject to the controlled object, and speed up the parameter tuning.
  Keywords: proportion integration differentiation; firefly algorithm improved by Newton’s law of universal gravitation; deep belief network; parameter self-tuning.
  
  
• An Improved Whale Optimization Algorithm for Distributed Assembly Flow Shop with Crane Transportation  
  by Qing-hua Li, Jun-qing Li, Qingke Zhang, Peng Duan, Tao Meng 
  Abstract: In this study, we investigate a classical distributed assembly flow shop scheduling problem with crane transportation. The objectives are to minimize the weighted value of the makespan and the energy consumptions. An improved whale optimization algorithm (IWOA) which embedded with a simulated annealing (SA) algorithm is proposed to solve the considered problem. First, a clustering method is applied to divide the solutions to improve the performance of the algorithm. Then, a right shift heuristic is developed to reduce the number of machine switches, therefor decreasing the energy consumption. In addition, two novel crossover operators, namely, factory crossover and solution crossover, are designed to increase the overall performance of the proposed algorithm. Furthermore, a SA-based global search heuristic is embedded in the algorithm to enhance its exploration abilities. Finally, several real-world instances were generated to test the performance of the proposed algorithm. The experimental results show that this algorithm has better performs better than other comparable algorithms.
  Keywords: distributed assembly flow shop scheduling; crane; energy consumptions; whale optimization algorithm.
  
  
• Linearisation of Three Phase Horizontal Gravity Separator  
  by Janakiraman Srinivasan, Devanathan Rajagopalan 
  Abstract: Control of nonlinear systems through linearisation has a wide application in process operations. The idea is that once linearised at an operating point, linear theory can be applied for control. Three phase horizontal gravity separator (TPHGS) system with its nonlinear characteristics can be a candidate for linearisation. Approximate linearisation approach due to Kang and Krener is utilized, to linearise the dynamic model of the separator. Approximate linearisation avoids the zero dynamics problems that might arise in exact feedback linearisation. Starting with a differential equation model of TPHGS, a state space model of TPHGS is obtained through a special transformation. Considering deviation around an operating point, a control affine model is obtained. Quadratic linearisation is then applied to the control affine model, using coordinate change and input transformations. Quadratic linearisation leads to a linearised system with only third and higher order nonlinearities in deviations present which can be considered negligible. A numerical example together with a Matlab simulation shows the effectiveness of proposed linearisation.
  Keywords: Three Phase Horizontal Gravity Separator; TPHGS; State space analysis; Nonlinear systems; Approximate linearisation; Quadratic linearisation.
  
  
• Load frequency controller for multisource interconnected nonlinear power system incorporating FACTs devices  
  by Arkan Ahmed Hussein, Naimul Hasan, Ibraheem Nasirudin, Shuaib Farooq 
  Abstract: The load frequency control for two area multisource interconnected power system model incorporating flexible AC transmission system (FACTs) devices is presented in this paper. The slow response of governor, the power swings and frequency oscillations tend to take longer time to settle to normal condition post disturbances. To overcome this condition, application of FACTs devices for frequency stabilisation under a small perturbation is studied and investigations are carried out on two area multisource interconnected power system model. The controller gains are tuned using genetic algorithm and the effect of FACTs devices for damping of power swings and frequency oscillations in an interconnected power system is studied and compared by the detailed analysis of power system dynamics. The power system model consists of thermal, hydro and doubly fed induction generator (DFIG)-based wind plant in each area. Governor deadband (GDB) and generation rate constraint (GRC) is also considered to study the effect of nonlinearity on the power system dynamics. It has been observed that FACTs enabled power system is very effective in damping out the power swings and local frequency oscillations caused due to disturbance in load. An improvement in the transient response and tie-line power oscillations is also quite appreciable.
  Keywords: DFIG; flexible AC transmission system; FACTs; generation rate constraint; GRC; genetic algorithm; multisource interconnected power system; super-conducting magnetic energy storage; SMES; load frequency control; LFC; unified power flow controller; UPFC.
  DOI: 10.1504/IJAAC.2020.10027587
   
  
• Propeller speed estimation for unmanned aerial vehicles using Kalman filtering  
  by Matija Krznar, Denis Kotarski, Danijel Pavkovic, Petar Piljek 
  Abstract: This paper presents an online propeller speed estimation system for a multirotor unmanned aerial vehicle (UAV) equipped with brushless DC (BLDC) motors and powered by a lithium-polymer battery pack. Propeller speed estimation is based on battery drain current measurement extended with averaged state-space model of brushless DC motor utilised within a Kalman filter-based state estimator. Based on the BLDC motor and propeller physical parameters and utilising corresponding mathematical model, the estimation system is implemented within the flight computer on board the UAV. The proposed propeller speed estimation algorithm is verified experimentally for a wide range of propeller operating regimes, which has shown that the proposed method is able to provide efficient estimation of UAV propeller speed.
  Keywords: UAV propulsion; speed estimation; Kalman filtering; BLDC motor.
  DOI: 10.1504/IJAAC.2020.10027588
   
  
• Adaptive control designed by online solving for Riccati and Lyapunov equations with nonlinear flight body  
  by Mohamed Fawzy Ahmed, Hassen Taher Dorrah 
  Abstract: The aim of this paper is to control the path for nonlinear missile model in the pitch channel using model reference adaptive control (MRAC) and L₁ adaptive control with linear quadratic regulator time-varying (LQRTV) strategy. Linear time-varying (LTV) model is designed where their parameters are varying with time. The nonlinear flying body, LTV model, and two adaptive control structures are modelled mathematically in the MATLAB-Simulink environment. LQRTV optimal control is designed using LTV model by online solving of Riccati Equation to get time-varying state feedback gain K(t). Adaptive control structures are designed using closed loop LTV model by online solving of Lyapunov equation to get time-varying Lyapunov gain matrix P(t). LQRTV and Lyapunov weight matrixes are tuned by Simulink design optimisation method. The results of two adaptive controls with the nonlinear flying body are compared. The wind effect and the dynamic uncertainties are researched.
  Keywords: nonlinear missile model; linear time-varying model; LTV; linear quadratic regulator time-varying; LQRTV; model reference adaptive control; MRAC; L₁ adaptive control; simulink design optimisation method; wind effect; dynamic uncertainties.
  DOI: 10.1504/IJAAC.2020.10027589
   
  
• Optimal robust control approaches for a geostationary satellite attitude control  
  by Naeimeh Najafizadeh Sari, Hadi Jahanshahi, Mahdi Fakoor, Christos Volos, Peyman Nikpey 
  Abstract: In this paper, two-optimal robust fuzzy proportional-integralderivative (FPID) and linear-quadratic regulator (LQR) controllers have been implemented for attitude control of a geostationary satellite, utilising momentum wheels. In the designed FPID controller, two fuzzy inference engines have been used, from which the second engine, is accounted to control the satellite attitude in severe deviations and preventing the system from instability. The designed FPID controller is optimised using the multi-objective genetic algorithm (MOGA) based on desired objective functions, which are deviation error from equilibrium states and control efforts. The optimal FPID controller is designed in such a way that while making extremum the desired objective functions, it also provides an appropriate controlling performance. Throughout designing robust LQR controller, design matrices of R and Q are selected in such a way to form a balance between the made control efforts and the settling time of the system.
  Keywords: fuzzy PID controller; robust LQR controller; genetic algorithm; multi-objective optimisation; geostationary satellite attitude control.
  DOI: 10.1504/IJAAC.2020.10027590
   
  
• Robust controller design for nonlinear twin rotor control system using quantitative feedback theory  
  by Jitendra Sharma, Bhanu Pratap 
  Abstract: This paper presents a robust controller for twin rotor control system (TRCS) subject to parametric uncertainty. TRCS exemplifies a class of multiple-input-multiple-output (MIMO) system having complex nonlinearity and cross-coupling effects. The linearised form of TRCS model is decoupled into two single-input-single-output (SISO) systems. Using quantitative feedback theory (QFT), the robust controller and pre-filter are designed for the two SISO subsystems to satisfy minimum gain and phase margin, tracking specifications for robust performance, actuator saturation, fast convergence, input and output disturbance rejection and sensor noise attenuation. QFT is a new and innovative robust technique based on Nichols chart in frequency domain. This approach achieves desired robust controller design over a specified range of system parametric uncertainty in spite of input and output disturbances and noise. QFT-based controller and pre-filter are designed for the required specifications of robust stability and robust tracking. Additionally, a proportional-integral-derivative (PID) controller is augmented for the nonlinear model of TRCS to compare the results of the two controllers. A detailed comparative evaluation has been worked out between the two controllers applied to the nonlinear model of the TRCS with the help of simulation studies.
  Keywords: nonlinear coupled system; PID controller; pre-filter; quantitative feedback theory; QFT; robustness; twin rotor control system; TRCS.
  DOI: 10.1504/IJAAC.2020.10027591
   
  

Special Issue on: Data-driven Intelligent Optimisation Methods and Applications

• Binary particle swarm optimization and extreme learning machine for paraquat-poisoned patients diagnosing  
  by Xuehua Zhao, Xin Tan, Zhen Li, Xu Tan, Qian Zhang, Huiling Chen, Lufeng Hu, Shuangyin Liu 
  Abstract: The diagnosis of paraquat-poisoned patients is one of important problems in medical diagnosis field. Current methods identify the paraquat-poisoned patients depending on paraquat content in human. However, the lack of such methods is treating paraquat-poisoned patients as healthy person when little paraquat content in body. Here, a new diagnostic method for paraquat-poisoned patients is proposed, which fuses on gas chromatography-mass spectrometry, binary particle swarm optimization and extreme learning machine together. In the proposed method, the data is collected by gas chromatography-mass spectrometry, the binary particle swarm optimization is adopted to select the excellent feature sets and the extreme learning machine is adopted to identify the paraquat-poisoned patients. In our experiments, two measures, which are accuracy and sensitivity, are used to evaluate our method, and we also made comparisons with four algorithms. The experimental results show that our method has better performance than other four methods.
  Keywords: medical diagnosis; paraquat-poisoned patients; feature selection; extreme learning machine; particle swarm optimization.
  
  
• Recharge strategies for the electric vehicle routing problem with soft time windows and fast chargers  
  by Teng Ren, Shuxuan Li, Yongming He, Chenglin Xiao, Ke Zhang, Guohua Wu 
  Abstract: At present, under the pressure of environmental pollution, logistics enterprises are beginning to use electric vehicles for various distribution services due to their low energy consumption, and environmentally friendly nature. Considering the fact that the quality of service and charging strategy have an important impact on the electric vehicle routing problem, to improve the efficiency of electric vehicles in logistics distribution networks, we investigate a multi-objective electric vehicle routing problem with soft time windows and fast charging stations (EVRPSTW-FC): mixed integer linear programming is established to minimise total logistics energy consumption.To solve the proposed model, a hybrid adaptive genetic algorithm (HAGA) is proposed. The performance of HAGA is compared and tested with benchmark examples, and the results verify the feasibility and effectiveness of the proposed model and solution algorithm.
  Keywords: electric vehicle;hybrid adaptive genetic algorithm;vehicle routing problem; soft time windows;.
  
  
• Design of a PV module block using the industrial automation PLC for PV system application  
  by Youness Ouberri 
  Abstract: The photovoltaic (PV) module parameters extraction for the PV modeling and simulation, are very important for the development, improvement, and control of the PV systems. This paper proposes a novel industrial automation programmable logic controller (PLC) based modeling using the single diode model. The main contributions of this work are: a) geometrical PV cell parameters extraction, b) PV modeling using automation PLC software. To validate the accuracy of the proposed approach, the extracted parameters will be compared to those extracted in previous studies of a multi-crystalline PV module, and to validate the PV modeling using automation software, the current-voltage (I-V) curves and the power-voltage (P-V) curves for several irradiation levels at 25
  Keywords: PV modeling; Parameters extraction; Automation PLC; HMI.
  
  
• Optimized Data-driven Terminal Iterative Learning Control based on Neural Network for Distributed Parameter Systems  
  by Xi-sheng Dai, Lan-lan Liu, Zhen-ping Deng 
  Abstract: In this paper, a data-driven iterative learning control with neural network-based optimization method for distributed parameter systems is presented to solve a class of problems caused by the imprecise mathematical model. The forward difference format is used to establish a linear relationship between input and output data, which is the only information available. However, this also leads to an unknown parameter matrix of the system. To overcome this problem, the radial basis function neural network is used to form a mappingrelation from the desired output to the desired input, and the iterative learning algorithm of neural network weight is obtained by optimizing the system performance indexes. Then, a detailed theoretical analysis based on composite energy function is given. Moreover, unlike traditional iterativelearningcontroltask trackingthe wholetrajectory,trackingtime terminal is taken into account in this paper. Finally, simulation results show the feasibility of the theory.
  Keywords: data-driven control; iterative learning control; distributed parameter systems; neuralrnnetwork; convergence.
  
  
• Study of Braking Strategy Considering Comfort  
  by Shenpei Zhou, Haoran Li 
  Abstract: A driving safety model considering comfort during the driver's actual driving experiences is established. The value of acceleration is used to measure the comfort. In order to balance the safety and comfort of drivers during the braking process, the model is setup by multi-objective optimization method of combining with driving comfort, braking distance and pedal force. Then the output of the model is analyzed by Memetic algorithm to make the braking distance smaller than the safety distance, and the driving comfort is optimized as well. The results of experiment show that the best solution of the model can satisfy the constrains of the comfort and safety distance at the same time. The braking strategy proposed in this paper is feasible and practical.
  Keywords: vehicle dynamics model; multi-objective optimization; Memetic algorithm; driving comfort.
  
  
• The Impact of Horizontal R&D Cooperation on the Climbing of Industrial Cluster Supply Chain?from the Perspective of Evolutionary Game Theory  
  by Juanli Lan, Shi Cheng, Bingxuan Wang 
  Abstract: Supply chain upgrade is a transformation process of supply chain operation efficiency and value realization. The upgrading of industrial cluster supply chain is of great practical significance to the development of China's cluster enterprises. As a kind of spatial economic organization with geographical proximity and industrial relevance, supply chain network and industrial cluster create a favorable platform for enterprise cooperation. This paper studies the R&D cooperation of industrial clusters based on the perspective of supply chain, constructs the evolutionary game model of R&D cooperation of horizontal enterprises in the cluster, and conducts numerical simulation analysis. The results show that: the impact mechanism of R&D investment, R&D cooperation risk, mutual trust level, R&D cooperation knowledge spillover, enterprise knowledge absorption capacity and R&D cooperation cost respectively to horizontal enterprise R&D cooperation in industrial cluster supply chain network. Finally, combined with the impact mechanism, it puts forward suggestions for realizing the strategy and path of the supply chain climbing of China's industrial clusters.
  Keywords: Industrial cluster; cooperative R&D; supply chain climbing.
  
  
• Self-adaptive Wolf Pack Algorithm based on Dynamic Population Updating for Continuous Optimization Problems  
  by Jinqiang Hu, Husheng Wu, Renjun Zhan, Yongli Li, Rafik Menassel 
  Abstract: Wolf Pack Algorithm (WPA) is a relatively new swarm intelligence-based algorithm for solving complex continuous optimization problems as well as real-world optimization problems. The basic WPA and its variants are prone to trap into local optima and premature convergence when tackling multi-modal functions due to diversity loss problem and imbalance between exploration and exploitation. Inspired by the idea of integrating the heuristic information and stochastic strategies to balance exploration with exploitation, we propose a self-adaptive WPA based on dynamic population updating strategy (SWPA-DU). First, the self-adaptive chaotic scouting behavior is designed to develop the global exploration of scout wolves. Second, a novel Cauchy perturbation operator is proposed to generate a few mutation besieging wolves, which not only enhances the capability of jumping out of local optima but also improves local exploitation. Third, a dynamic population updating strategy is invented to improve diversity. Numerical experiments with a suit of benchmark functions and practical applications are performed to verify the effectiveness and advancement of the proposed algorithm. The experimental results indicate that SWPA-DU obtains superior performance on both multi-modal and high-dimensional problems over the compared algorithms.
  Keywords: swarm intelligence; wolf pack algorithm; self-adaptive chaotic scouting behavior; Cauchy perturbation operator; dynamic population updating.
  
  

Special Issue on: ICCSDET-2018 Modelling and Applications of Nonlinear Control Systems

• A new conservative chaotic dynamical system with lemniscate equilibrium, its circuit model and FPGA implementation  
  by Sundarapandian Vaidyanathan, Esteban Tlelo-Cuautle, P.S. Godwin Anand, Aceng Sambas, Omar Guillén-Fernández, Sen Zhang 
  Abstract: A new conservative dynamical system exhibiting chaos properties is introduced in this work. The proposed nonlinear plant with conservative chaos has a lemniscate of equilibrium points. It is noted that the new 3-D nonlinear plant exhibits hidden attractors. It is established that the new nonlinear plant exhibits conservative chaos and its properties are studied via bifurcation diagrams and Lyapunov exponents. The new nonlinear plant with lemniscate equilibrium exhibits multi-stability and coexisting attractors. A circuit model using MultiSim and FPGA implementation of the new nonlinear plant with lemniscate equilibrium are carried out for enhancing practical implementation.
  Keywords: Chaos; chaotic systems; FPGA implementation; circuit simulation.
  
  
• Three-level (NPC) Shunt Active Power Filter Based on Fuzzy Logic and Fractional-order PI Controller  
  by Sihem Ghoudelbourk, Ahmad Taher Azar, Djalel Dib 
  Abstract: The power electronics converters are widely used in industrial equipment; such equipment presents a non-linear load and generates large harmonic currents and low power factor. To solve these problems, many methods have been proposed of the power filter for integrated harmonic compensation and amelioration of power quality. In this paper, a three-level neutral point clamped (NPC) shunt active power filter is proposed which injects into the network a current equal to that absorbed by the polluting load, but opposed to the phase. Thus, it is to prevent current disrupters of pollutants to flow through the impedance of the network making the sinusoidal network side current. The three-level neutral-point-clamped (NPC) inverter hysteresis-based control shunt active power filter offers enormous advantages compared to the two-level inverters. The regulation and the stability of the DC voltage across capacitor of the power supply filter during in transient states and under various operating conditions is ensured by fuzzy logic controller and then by a fractional order Proportional Integral (PI) controller. A comparative study was carried out and the comparison criteria are the total harmonic distortion (THD) in the current line and the stability of the voltage during the load variation. A series of simulations is presented and discussed to show the performance of this control strategy by a fractional order PI controller. The obtained results showed the efficiency of the proposed shunt active filter based on a three-level inverter (NPC) using a fractional order controller.
  Keywords: Three-level (NPC); Shunt Active Filter; Fuzzy logic control; Fractional-order Proportional Integral (PI) controller.
  
  
• H∞ performance analysis and switching control design for uncertain discrete switched time-delay systems  
  by Hao-Chin Chang, Chang-Hua Lien, Ker-Wei Yu 
  Abstract: The H∞ performance analysis and switching control of uncertain discrete switched time-delay systems with linear fractional perturbations are studied in this paper. The design of a simple switching signal is developed to acquire LMI conditions which achieve H∞ performance and switching control of discrete switched time-delay system. The conservativeness of obtained results can be improved by using the delay-partitioning approach. The using LMI variables number is less than our past proposed nonnegative inequality approach. Finally, we use some numerical examples to illustrate the major improvement of the developed results.
  Keywords: H∞ performance analysis; switching control; design of switching signal; discrete switched systems; time delay; delay-partitioning approach.
  
  
• Occasional stabilization of limit cycle walking and control of chaos in the passive dynamics of the compass-gait biped model  
  by Hassène Gritli 
  Abstract: The passive dynamic walking is a bipedal locomotion of a compass-gait biped robot as it goes down some inclined surfaces without any source of actuation or control. Such biped robot is a two-degree-of-freedom impulsive hybrid mechanical system known to possess passive limit cycle walking reminiscent of human walking. It is known also that the compass-gait model exhibits attractive cyclic walking patterns and complex phenomena, namely chaos and bifurcations. This work is concerned with the stabilization of limit cycles of the passive dynamic walking of the compass-gait model and then the control of chaos. A new stabilization process of the limit cycle walking is developed based on self-detection of the fixed point of the (un)stable limit cycle and on energy-shaping-based trajectory-tracking controller. The control process is applied in the beginning of the swing stage during a desired short time interval making hence the compass-gait biped robot to be completely passive on the remaining swing phase. We demonstrate that such occasional stabilization of the limit cycles considerably increases the energetic efficiency of the bipedal locomotion. We show also that the proposed control method allows the compass-gait biped robot to walk efficiently and with a periodic gait down sloped surfaces of different angles.
  Keywords: Compass-gait biped model; Passive dynamic walking; Hybrid limit cycle; Chaos; Stabilization; Occasional nonlinear control.
  
  
• Machine Learning Based Novel DSP Controller for PV Systems  
  by Subramanya Bhat 
  Abstract: As fossil fuels are getting depleted it is very much essential to harvest solar energy. The harvesting and conversion methods available in literature are simulation based. A very few work has been reported using hardware circuitry. However, Machine Learning based DSP controller for solar energy harvesting is not available in literature. In the proposed study, Machine Learning based DSP controller is implemented. The Genetic Algorithm (GA) based DSP controller has been designed for enhancing the efficiency of Solar PV. In the proposed work, Perturb & Observe (P & O) technique and Genetic Algorithm (GA) have been considered to achieve maximum power point and precise control parameters of PID controller respectively. Single DSPTMS320F28377s has been used to implement both P & O and GA and it is revealed that the proposed DSP based hardware model provides better speed, efficiency and reliability than the existing simulation based controller. The proposed work will bring a paradigm shift in solar energy harvesting and control.
  Keywords: converter; tuning; control algorithm.
  
  
• Optimal control based on multiple models approach of chaotic switched systems, application to a stepper motor  
  by Moez Feki 
  Abstract: In this paper, we are interested in the control of chaotic switched systems with application to a stepper motor. The aim of the control is to determine the optimal sequence of switching instants in order to bring the chaotic behavior of the system to a periodic one. The determination of the optimal sequence will follow two steps using an optimization algorithm, the Hamiltonian system and the derivative of a performance criterion over the switching instants. The nonlinear switched system is modeled by a multiple linear time-invariant models approach to make possible to calculate the gradient of the cost function and therefore to determine the optimal instants of switches. Simulations are applied to a stepper motor to illustrate the results.
  Keywords: multiple models; linear time invariant model; optimal control; chaotic switched systems.
  
  
• Low Power Pulsed Flip-Flop with Clock Gating and Conditional Pulse Enhancement  
  by Kuruvilla John, Vinod Kumar R. S., Kumar S. S. 
  Abstract: A clock system consumes above 25% of the total system power. Flip-Flops (FFs) are widely used as the basic storage element in all kinds of digital structures. The use of pulse-triggered flip-flops (P-FFs) in digital design provides better performance than conventional flip-flop designs. This paper presents the design of a new power-efficient implicit pulse-triggered flip-flop suitable for low power applications. Two important features are embedded in this flip-flop architecture. Firstly, the enhancement in width and height of trigger pulses during specific conditions gives a solution for the longest discharging path problem in existing P-FFs. Secondly, the clock gating concept reduces unwanted switching activities at sleep/idle mode of operation and thereby reducing dynamic power consumption. The post-layout simulation results in cadence software based on CMOS 90-nm technology shows that the proposed design features less power dissipation and better power delay performance (PDP) when compared with conventional P-FFs. This paper also presents a comparative study on the performance of implicit and explicit pulse flip-flop designs. The maximum power saving of proposed design against conventional implicit and explicit design is up to 18.45% and 58.49% respectively.
  Keywords: pulse flip-flop; low power; implicit; explicit; clock gating.
  
  

Special Issue on: Intelligent Optimisation Methods for Scheduling Problems

• Research of Local Shadow MPPT of Photovoltaic Array based on EV-IKMTOA  
  by Lingzhi Yi, Dongfang Zhou, Chaodong Fan, Liyun Qiu 
  Abstract: The general algorithm is easy to fall into the local extremum when it is searching in the local shadow environment, and it is difficult to achieve the maximum power point output of the photovoltaic array. This paper proposes a multi-peak MPPT?Maximum power point tracking?optimization strategy based on the improved molecular dynamic optimization algorithm. This algorithm firstly uses the current in the P-I curve as the search area to narrow the particle search range. On this basis, the particle variance value is calculated to adjust the particle velocity to make the particle distribution in the population uniform, Meanwhile, the number of particles in the population and the number of iterations of the population are coordinated from the whole?so that the algorithm can quickly track to the global optimal vicinity, Then, the improved perturbation observation method (IACSA) is used to search for the global optimal solution. In order to enable the photovoltaic system to respond to the dynamic changes of the external environment in time, the algorithm regards the external illumination intensity and temperature as the restart conditions of the algorithm
  Keywords: Molecular dynamic algorithm; photovoltaic multi-peak MPPT global optimization; Disturbance observation; variance; local shadow;.
  
  
• An Evolutionary Algorithm for Hybrid Flowshop Scheduling Problem with Consistent Sublots  
  by Xinli Zhang, Biao Zhang, Leilei Meng 
  Abstract: Lot Streaming is the most often used technique to support the time-based strategy in the modern manufacturing system, which can split the jobs (or lots) with larger size into several sublots with smaller size. With this manufacturing technique, this paper studies a hybrid flowshop scheduling problem with consistent sublots (HFSP_CS). With the consideration of the integrated optimization of lot sequencing and lot splitting, a mixed-integer linear programming (MILP) model is established with the objective of minimizing the total flowtime. Since the NP-hard property of the problem, a solution method integrating the migrating birds optimization (MBO) and variable neighborhood descent (VND) algorithms is developed. Moreover, by taking into account the problem-special characteristics, the two-layer coding mechanism and a corresponding initialization method are designed. And some heuristic methods are also presented in the decoding process. In the computational study, the effectiveness of the proposed algorithm is evaluated by comparing with CPLEX solver and other state-of-the-art algorithms.
  Keywords: hybrid flowshop; lot streaming; consistent sublots; migrating birds optimization;.
  
  
• Bi-level programming model for post-disaster emergency supplies scheduling with time windows and its algorithm  
  by Fuyu Wang, Yan Li, Yan Li, Jingjing Chen 
  Abstract: Aiming at the emergency supplies scheduling problem in disaster situation, a bi-level programming model with time window constraints is built by considering the actual characteristics and demand of emergency material dispatching, with the minimum system response time as the upper objective and the minimum total system cost as the lower objective. According to the characteristics of mutual correlation and restriction between the upper and lower levels of the emergency supplies scheduling model, a two-stage heuristic algorithm is designed. At the first stage, the algorithm uses the clustering method for location-allocation and at the second stage uses the improved glowworm swarm optimization algorithm for transportation route arrangement. Then the simulation experiment is performed, which shows that the model and algorithm can effectively solve the post-disaster emergency supplies scheduling problem, and the designed algorithm has good performance and high computational efficiency.
  Keywords: emergency supplies scheduling; time windows; bi-level programming; improved glowworm swarm optimization algorithm.
  
  

Special Issue on: Hybrid Intelligent Techniques Foundations, Applications and Challenges

• K-Anonymity Scheme for Privacy Preservation (KASPP) in Location Based Services on IoT Environment  
  by Ayan Kumar Das, Ayesha Tabassum, Sayema Sadaf, Ditipriya Sinha 
  Abstract: Location based services have important impacts on many applications of Internet of Things. In these services location of users are revealed in front of third party server that makes the privacy of user vulnerable. It is required to collect real time data regarding ongoing events to response the query of user. Wireless Sensor Network is most popular to collect the data sensed by the sensor nodes and assist the third party server to response the query of user. The energy constraint sensor nodes motivate the researchers to design energy efficient routing. The proposed scheme of this paper has designed two aspects of Internet of Things environment- a k-anonymity based privacy preservation scheme for quality query response service and an energy efficient secure routing to collect real time data from sensor nodes in order to response the query of user. The performance analysis shows that the proposed scheme performs better with compared to existing schemes.
  Keywords: Privacy preservation; Degree of Anonymity; Wireless Sensor Network; Internet of Things; Greedy forwarding technique; Ego of Data; Void problem.
  
  

Special Issue on: Swarm Intelligence-based Optimisation and Scheduling in Networked Systems

• Multi-objective Flexible Flow Shop Batch Scheduling Problem with Renewable Energy  
  by Xiuli Wu, Xiao Xiao, Qi Cui 
  Abstract: Renewable energy is an alternative for the non-renewable energy to reduce the carbon emission in manufacturing system. How to make an energy-efficient scheduling solution when renewable and non-renewable energy drive the production alternatively is of great importance. In this paper, a multi-objective flexible flow shop batch scheduling problem with renewable energy (MFBSP-RE) is studied, variable processing time and handling time are taken into account. To begin with, the mathematical model is formulated to minimize the carbon emission and makespan simultaneously. Then, a hybrid non-dominated sorting genetic algorithm with variable local search (HNSGA-II) is proposed to solve the MFBSP-RE. The operation based encoding method is employed. A low-carbon scheduling algorithm is presented. Besides the crossover and mutation, a variable local search is employed to improve the Pareto set. Finally, the results of experiments show that the proposed HNSGA-II outperforms the standard NSGA-II algorithm and can solve the MFBSP-RE effectively and efficiently.
  Keywords: flexible flow shop scheduling problem; batch scheduling; HNSGA-II; renewable energy; handling time.
  
  
• Hybrid Fruit Fly Optimization Algorithm for Field Service Scheduling Problem  
  by Bin Wu 
  Abstract: With the development of the online to offline business model, field services related to individual customer needs and customized services are becoming increasingly important. The field service scheduling problem is the core problem in field service. However, it has not been considered that scheduling results are affected by the skill proficiency of workers in past studies. Therefore, we propose a model considering the skill level of workers based on the optimization goals of travel time, service time, and waiting time. A hybrid fruit fly optimization algorithm (FOA) is proposed to optimize the model. Based on the features of the problem and merit of the algorithm, a matrix encoding method is designed. Three search operators are then proposed and the smell-based search strategy and vision-based search strategy for the FOA are redesigned. Additionally, an initialization operator based on the nearest-heuristic algorithm and a post-optimization process based on the 2-opt and or-opt algorithms are constructed to improve the performance of the FOA. Finally, the proposed operators and strategies are compared and analyzed, and the hybrid FOA is compared with other algorithms through simulation experiments. The simulation results demonstrate that the proposed hybrid Fruit Fly Optimization algorithm is an effective method to solve the field service scheduling problem.
  Keywords: Field Service Scheduling Problem ; Fruit Fly Optimization Algorithm; Intelligent Computing.
  
  
• A Comparative Study on Evolutionary Algorithms for the Agent Routing Problem in Multi-point Dynamic Task  
  by Sai Lu, Bin Xin, Lihua Dou, Ling Wang 
  Abstract: The agent routing problem in multi-point dynamic task (ARP-MPDT) proposed recently is a novel permutation optimization problem. In ARP-MPDT, a number of task points are located in different places and their states change over time. The agent must go to the task points in turn to execute the tasks and the execution time of each task depends on the state. The optimization objective is to minimize the time for the agent to complete all the tasks. In this paper, a more comprehensive comparative study was conducted. More evolutionary algorithms are redesigned and tried to solve this problem, including a permutation-based genetic algorithm (GA), a variant of particle swarm optimization (PSO) and three variants of estimation of distribution algorithm (EDA), one of which used dual-model (DM-EDA). Comparative tests confirmed that the DM-EDA had a stronger adaptability than the other algorithms although GA performed better for the large-scale instances. PSO had a poor effect of searching the better solutions.
  Keywords: Multi-point dynamic task; Estimation of distribution algorithm; Dual-model.
  
  
• MR brain image segmentation using elite kinetic-molecular theory optimization algorithm  
  by Chaodong Fan 
  Abstract: Because the imaging process is complex, the presence of noise is inevitable MR brain images. Although the cross-sectional projection of traditional Otsus method noise immunity strong, it is difficult directly to brain MR image segmentation as a single thresholding. In this regard it, elite kinetic-molecular theory optimization algorithm, is put forword. Firstly, the design of multi-threshold Otsus sectional projection method, and then use the kinetic theory of elite optimization algorithm for optimal threshold segmentation to improve efficiency. The Experiments show that the algorithm is computationally efficient, better noise immunity, capable of MR brain images with different noise were better segmentation.
  Keywords: Optimization algorithm; Elite mechanism; Kinetic-molecular theory optimization algorithm; Image segmentation; Otsu’s method.
  
  
• A Hierarchical Parallel Evolutionary Algorithm of Distributed and Multithreaded Two-level Structure for Multi-satellite Task Planning  
  by Man Zhao, Dongcheng Li 
  Abstract: The aim of multi-satellite task planning is to study how to distribute limited resources of satellites and payloads and execution time for observation missions to be completed within a limited set of available satellites so as to best satisfy the observational demand. Aiming at the shortcomings of the current study on multi-satellite task planning, this paper proposes a hierarchical parallel evolution algorithm framework which is based on a distributed and multi-threaded two-level structure. It adopts parallel communication flow and task-distribution strategy of multi-machine, multi-core and two-level structure. The distributed parallel evolution model work among multi-machines, whereas the multi-threaded parallel evolution model work among multi-cores to reduce the communication overhead of the parallel system while maintaining the global optimization of the algorithm. The result of the experiment showed that the multi-satellite task planning evolutionary optimization model established in the paper is effective. Subsequently, it was proven that the hierarchical parallel-evolving algorithm proposed by the paper can greatly cut down the time consumed for the evolution and improve the algorithm solving efficiency, which can effectively solve both the multi-satellite task planning issue and optimization problems in other fields. It is thus of important use value.
  Keywords: multi-satellite task planning; distributed; multi-threading; parallel computing; differential-evolution algorithm.
  
  
• A Comparative Evaluation of PID Based Optimization Controller Algorithms for DC Motor  
  by Sajid Rakih Ahamed, Poovaneswaran Parumasivam, Molla Shahadat Hossain Lipu, M.A. Hannan, Pin Jern Ker 
  Abstract: DC motors are generally used in industrial activities due to its simple structure and higher reliability. However, a DC motor is unable to control the operational speed as per required by the system. Proportional Integral Derivative (PID) controller is used in common for motor application due to its ease of control and operation. Nevertheless, the setting of PID parameters in DC motor control is difficult when the system is highly non-linear and complex. Hence, the development of an improved auto-adjusted PID controller is an urgent necessity. This paper develops a robust PID controller-based optimization algorithms for DC motor for finding the optimum parameters setting of PID controller. A comprehensive comparative analysis between PID based backtracking search algorithms (BSA) and PID based particle swarm optimization (PSO) is provided. The conventional PID, PSO PID, and BSA PID models are evaluated based on overshoot reduction and settling time under ramp step and unit step speed variations. The results demonstrate that the PID controller with BSA achieves an optimum output with better dynamic and static performance.
  Keywords: DC Motor; Speed Controller; PID Controller; Particle Swarm Optimization; Backtracking Search Algorithms; Objective Function; Overshot; Settling time.
  
  
• Swarm Intelligence-Based Optimization Algorithms: An Overview and Future Research Issues  
  by Jinqiang Hu, Husheng Wu, Bin Zhong, Renbin Xiao 
  Abstract: Swarm intelligence-based optimization algorithms, inspired by the collective intelligent behaviors of biology groups, have been widely recognized as efficient optimizers for many complex problems, e.g., dynamic optimization problems, large-scale optimization problems and many-objective optimization problems. Swarm intelligence-based algorithms are the generic concepts to represent a range of metaheuristics with population-based iterative process, guided random search and parallel processing. This paper conducts an in-depth analysis of universality and difference of existing swarm intelligence-based algorithms. It also provides a systematical survey of some well-known algorithms. In addition, the expected research issues such as theoretical analysis, hybridization strategy and complex problems optimization are discussed thoroughly to inspire future study and more extensive applications.
  Keywords: swarm intelligence; optimization algorithm; universality; theoretical analysis; hybridization strategy; complex optimization problems.