International Journal of Automation and Control (47 papers in press)

Regular Issues

• Adaptive fuzzy control for the stabilization of chaotic systems  
  by Hanene Medhaffar, Moez Feki, Nabil Derbel 
  Abstract: In this paper, we investigate the stabilization of unstable periodic orbits of continuous time chaotic systems using adaptive fuzzy controllers. For this aim, we present a control method that can achieve the stabilization of an unstable periodic orbit (UPO) without any knowledge of the system model. Thus, a fuzzy adaptive linear controller is proposed based on time-delay feedback approach to obtain one that achieves UPO stabilization. Finally, we use reduced order sliding observer to estimate the necessary state for the controller construction. The efficiency of proposed methods is demonstrated using numerical simulations applied to Chuas system.
  Keywords: Chaos control; Fuzzy adaptive systems; Time-delayed state feedback, Sliding mode observer.
  
  
• Processor-in-the-Loop Co-Simulations and Control System Design for a Scaled Autonomous Multi-Wheeled Combat Vehicle  
  by Amr Mohamed, A.N. Ouda, Jing Ren, Moustafa El-Gindy 
  Abstract: This paper describes the design and implementation of PID and fuzzy logic controllers for tracking the desired heading angle for a scaled Autonomous Multi-Wheeled Combat Vehicle (AMWCV). The challenge of designing these control systems is to control the steering of the front four wheels individually in order to obtain the correct heading angle of the vehicle. The performance of the developed controllers is validated in the presence of noise and disturbance in order to evaluate their robustness. A Processor-In-The-Loop (PIL) co-simulation is conducted to permit and achieve a more realistic situation where the developed control algorithms are evaluated while running on a dedicated processor. The obtained results from both simulation and PIL are compared. This comparison will demonstrate the controller effectiveness in tracking the desired heading angles.
  Keywords: Autonomous vehicle; multi-wheeled combat vehicle; heading control; fuzzy logic; classical control; processor-in-the-loop.
  
  
• A Two-Phase Approach for the Design of Two-Degree of- Freedom of Robust Controller for Higher Order Interval System  
  by Mangipudi Siva Kumar, Danaboyina Srinivasa Rao, Manyala Ramalinga Raju 
  Abstract: This paper proposes a novel method for the design of the robust controller to retain both the robust stability and performance of the higher order interval system via reduced order model using the differential evolution (DE) algorithm. A stable reduced interval model is generated from a higher order interval system using DE in order to minimize the cost and reduce the complexity of the system. The reduced order interval numerator and denominator polynomials are determined by minimizing the Integral Squared error (ISE) using the DE. From reduced order interval model, a robust PI controller is designed based on the new stability conditions of interval system. The designed robust controller from the reduced order interval model will be attributed to the higher order interval system. The designed PI controller from the proposed method not only stabilizes reduced order model but also stabilizes the original higher order system. Finally, with the help of frequency domain method a pre-filter is constructed to improve the performance of interval system. The viability of the proposed methodology is illustrated through a numerical example for its successful implementation.
  Keywords: Interval system; Kharitonov’s theorem; Robust controller; model order reduction,Differential Evolutionary algorithm.
  
  
• Online Sensor Aging Detection using a Modified Adaptive Filter  
  by AQEEL MADHAG, Guoming Zhu 
  Abstract: Modern control systems heavily rely on sensors signals for feedback control, and therefore, sensor performance and fault diagnostics are essential. Degradation of sensor performance due to sensor aging affects the closed-loop system performance, reliability, and even stability. Sensor aging can be characterized by the gradual-variation of the sensor measurement noise covariance. This paper proposes a fault detection algorithm to detect online sensor performance degradation and failure due to sensor aging, where the sensor faults due to aging are characterized by slow variations of the sensor measurement noise covariance matrix. To be specific, the key feature of the proposed algorithm is online detecting gradual sensor performance degradation due to sensor aging by estimating the slowly-varying sensor measurement noise covariance matrix.rnThe proposed algorithm utilizes the information about the quality of weighted innovation sequence to estimate the slowly-varying sensor noise covariance. The iterative manner of the proposed algorithm leads to significant reduction of the computational load, reduced sensitivity to initial conditions and improved estimation accuracy, making it suitable for online applications. Simulation results show that the proposed algorithm is capable of estimating the slowly-varying sensor noise covariance for multiple-input and multiple-output systems with noise covariance varying linearly, exponentially, or linearly with sinusoid fluctuation. Furthermore, the proposed estimation algorithm shows a reasonable rate of convergence, better estimation accuracy and less computation load in contrast to published literature.
  Keywords: Sensor aging noise; covariance identification; Kalman filter; covariance matching; discrete time-varying system.
  
  
• An analytical approach to optimal control of nonlinear systems with input constraints  
  by Mehdi Mirzaei, Reza Mojed Gharamaleki, Sadra Rafatnia, Behrooz Alizadeh 
  Abstract: In this paper, a novel optimal control method with the analytical solution strategy is developed for a wide class of nonlinear systems with input constraints. First, an equivalent constrained optimization problem is formulated by performing a quadratic performance index including the control inputs and the predicted tracking errors. Then, the problem is analytically solved by using the Kerush-Kuhn-Tucker (KKT) theorem to find the optimal control law. For comparison, a computational technique based on calling the genetic algorithm (GA) is also provided to online solving the developed optimization problem. The proposed control method with two solutions is applied on a mathematical example and a chemical reactor which is a multi-input multi-output (MIMO) system. The results show that the proposed KKT-based predictive controller is effective from different aspects. Importantly, it is very fast, easy to solve and suitable for online implementation compared with the conventional nonlinear model predictive control (NMPC) method.
  Keywords: Constrained optimal control; Nonlinear systems; Prediction; Optimization; Analytical solution.
  
  
• Fuzzy rule based auto-tuned internal model controller for real-time experimentation on temperature and level processes  
  by Ujjwal Manikya Nath, Chanchal Dey, Rajani K. Mudi 
  Abstract: Recently internal model control (IMC) technique has been widely employed for various industrial close-loop control applications. Rewarding feature of IMC controller is that we need to tune only one parameter (close-loop time constant) for achieving the desired close-loop response. But, finding an appropriate value of is not an easy task. From the basic behavior of IMC based close-loop responses it is found that when the process variable is moving very fast towards the desired value, relatively larger value of (smooth control) is required to reduce the possible oscillations. In contrary, smaller value of (tight control) is preferred when the process response is quickly moving away from the set point to restrict its further deviation. Hence, to mitigate the limitation of conventional IMC tuning with a fixed , a fuzzy rule based auto-tuning scheme is proposed here for IMC-proportional integral derivative (IMC-PID) controller and its performance is validated through real-time experimentation on temperature and level control loops.
  Keywords: IMC-PID controller; fuzzy rule based tuning; auto-tuning IMC; real-time evaluation of temperature control loop; level control loop.
  
  
• Load Frequency Controller for Multi-source Interconnected non-linear Power System Incorporating FACTs Devices  
  by Arkan Ahmed Hussein, Naimul Hasan, Ibraheem Nasirudin, Shuaib Farooq 
  Abstract: The Load Frequency control for two area multi source 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 stabilization under a small perturbation is studied and investigations are carried out on two area multi source 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 Double Fed Induction Generator (DFIG) based wind plant in each area with different participation factor for total generation. Governor deadband (GDB) and generation rate constraint (GRC) is also considered to study the effect of non-linearity 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: Load frequency control ; DFIG; FACTs; GRC ; Genetic algorithm ; Multisource interconnected power system; SMES; UPFC.
  
  
• Propeller speed estimation for unmanned aerial vehicles using Kalman filtering  
  by Matija Krznar, Denis Kotarski, Danijel Pavkovic, Petar Piljek 
  Abstract: This paper presents an on-line propeller speed estimation system for a multi-rotor 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 utilized 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; BLDC motor; Kalman filtering.
  
  
• 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 the 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. Linear Time-Varying (LTV) model presents the best approximation for nonlinear missile flying body. The equations of motion for the nonlinear flying body, LTV model, and two adaptive control structures (MRAC and L1 adaptive control) with LQRTV strategy are modeled 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). MRAC and L1 adaptive controllers utilize Lyapunov design method to guarantee the stability of the closed-loop system and to decrease the parameters estimation error. LQRTV and Lyapunov weight matrixes are tuned by Simulink design optimization method to get the optimum values that achieve good tracking with pitch angle program. Two adaptive control structures with LQRTV strategy are able to compensate the actuator restrictions in pitch channel. The results of two adaptive control structures with the nonlinear flying body are compared and the wind effect is studied where wind velocity is summated to nonlinear missile velocity. The dynamic uncertainties are researched with two adaptive control structures by changing the aerodynamic coefficients.
  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 optimization method; wind effect; dynamic uncertainties.
  
  
• 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-integral-derivative (FPID) and Linearquadratic regulator (LQR) controllers have been implemented for attitude control system of a geostationary satellite, utilizing momentum wheels. In the designed FPID controller, two categories of fuzzy inference motors have been used, from which the second motor, is accounted to control the satellite attitude in severe deviations from equilibrium states and of preventing the system from instability. The designed FPID controller is optimized using the genetic algorithm (GA) based on desired objective functions. Since the number of objectives is more than one, the problem is converted to a multi-objective optimization in which GAs tries to optimize the desired objectives, simultaneously. In this problem, optimization functions consist of deviation error from equilibrium states along x-, y-, and z-axis of body coordinate system 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. This is true by considering its nonlinear terms and complexity. Throughout designing robust LQR controller by limiting and finding an upper bound for uncertainties and defining optimal values of the parameter γ, 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 optimization; Geostationary satellite attitude control.
  
  
• 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 linearized form of TRCS model is decoupled into two single-input-single-output (SISO) systems. Using quantitative feedback theory (QFT), the robust controller and prefilter 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; robustness; twin rotor control system.
  
  
• 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.
  
  

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: 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.
  
  

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.
  
  
• 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.
  
  
• 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.
  
  

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.