International Journal of Automation and Control (51 papers in press)

Regular Issues

• 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.
  
  
• 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.
  
  
• 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.
  
  
• An Improved Whale Optimisation 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 minimise the weighted value of the makespan and the energy consumptions. An improved whale optimisation 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 optimisation algorithm.
  DOI: 10.1504/IJAAC.2021.10030827
   
  
• 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.
  DOI: 10.1504/IJAAC.2021.10037863
   
  
• Minimizing weighted completion time on a single machine under uncertain weights  
  by Hui Wu, Bing Wang 
  Abstract: This paper has investigated the single-machine scheduling problem regarding the minimization of the total weighted completion time, with the known processing times, while weights are uncertain. Uncertainty in weights is modelled using a scenario set, which contains explicitly listed scenarios of weights (the discrete-scenario case) or the Cartesian product of the intervals that contain possible values of weights (the interval-data case). Two main criteria are investigated: minimizing the maximum objective function (min-max version) and minimizing the maximum regret (min-max regret version). The computational complexity of the min-max (regret) versions of the single-machine scheduling problem in the cases of the discrete scenario as well as interval data is discussed, respectively, and on this basis, the approximation of corresponding NP-hard problems is further analysed.
  Keywords: Single-machine scheduling; weighted completion time; min-max; min-max regret; complexity; approximation.
  
  
• A Novel Optimized Method for Speckle Reduction in Medical Ultrasound Images  
  by Shereena V B, Raju G 
  Abstract: Modern Medical science developed various Medical Imaging Techniques in the early detection, diagnosis and treatment of complex disorders in human body, which evolved from X-Ray to PET images. The treatment of various disorders is done through the analysis of clinical images so obtained from the said modalities. Out of these imaging modalities, the widely accepted one is the Ultrasound imaging modality because of its affordability and non-invasive nature. But the ultrasound image processing and analysis becomes a challenging task due to the presence of speckle noise. Various studies are going on to reduce the speckle noise in ultrasound image, but many of them suffer from limitations such as low computational efficiency and loss of crucial image features. This article, introduces the improved hybrid method to reduce Speckle Noise by using Kuan and Non-Local Means Filter. Though Non-Local Means has an attractive de-speckling performance, it eliminates the structured details in the image and cannot predict the edges correctly. So, in Hybrid Method the simple Kuan filter is used to sharpen the edges and thereafter the speckle noise elimination is done by using the NonLocal means. In addition, the performance of the proposed hybrid filter and its design parameters are optimized by using a meta-heuristic called Grey Wolf Optimizer (GWO). Comprehensive study on various ultrasound B-mode images are conducted to analyze the competence of the proposed method over a chosen set of well-known post filtering methods for Speckle reduction. The results of quantitative comparison using remarkable performance metrics and computation time demonstrated that the proposed method has an efficient denoising capacity and retains the essential image features with less time complexity.
  Keywords: Ultrasound Image; Speckle Noise; Multiplicative Noise; Performance Metrics; Spatial Filter; Transform domain Filter; Kuan Filter; Non-Local Means Filter; Grey Wolf Optimization; Hybrid Filter.
  DOI: 10.1504/IJAAC.2021.10036226
   
  
• Parameter Settings in Particle Swarm Optimization Algorithms: A Survey  
  by Shi Cheng 
  Abstract: In swarm intelligence, ``fair comparison'' is critical for the performance evaluation of algorithms. In this paper, the setting of parameters in particle swarm optimization (PSO) algorithms, which include the population size $S$, topology structure (number of neighbors $k$), inertia weight $w$, acceleration coefficient $c_{1}$, $c_{2}$, velocity constraint $V_{max}$, and the boundary constraint strategy, are reviewed and analyzed. Based on the analysis and discussion of parameters and the variants of PSO algorithms, a list of parameter settings of PSO algorithms and a recommendation of PSO comparison are given. To compare variants of PSO algorithms, a recommend solution maybe that all compared algorithms have the same number of population size and the maximum number of fitness evaluations, and the inertia weight $w$, acceleration coefficient $c_{1}$, $c_{2}$ are the same settings as its original version.
  Keywords: Swarm intelligence; particle swarm optimization; parameter investigation; performance comparison.
  
  
• Implementation of Discrete-Time Fractional-Order Derivative Controller for a Class of Double Integrating System  
  by Jaydeep Swarnakar 
  Abstract: The design of the fractional-order controller (FOC) is an important topic in the prevailing literature of system and control. The digital realization of the FOC involves the discrete-time approximation techniques of z-domain. In this paper, an alternative approach has been presented on contrary to the conventional approaches for discrete-time realization of the FOC through employment of the delta operator. The delta operator modelling offers a unified framework in system theory such that the discrete-time system amalgamates to its continuous-time counterpart at fast sampling rate. This captivating property of the delta operator has been capitalized in this work for implementing the FOC. In this paper, a fractional-order derivative controller has been designed to control a double integrator plant towards satisfying the specific design criterions of frequency domain. The design approach employs a reference model. The open loop transfer function of the reference model is given by the Bodes ideal transfer function. The design is accomplished in two stages. At first, the reference model is obtained from the given design specifications and the transfer function of the continuous-time fractional-order derivative controller is derived subsequently. In the next stage, the FOC has been realized in delta-domain involving continued fraction expansion method. The efficacy of the presented design methodology has been established through a comparative study with one of the conventional approaches pertaining to discrete-time implementation of FOC. The robustness of the closed loop controlled system is also tested against the uncertain plant gain. The essential simulation results have been presented using MATLAB.
  Keywords: fractional-order derivative controller; double integrator system; continued fraction expansion; delta operator.
  DOI: 10.1504/IJAAC.2021.10033936
   
  
• IMC based anti-windup controller for real-time hot air flow and level control loop  
  by Ujjwal Manikya Nath, Chanchal Dey, Rajani K. Mudi 
  Abstract: In this study a new designing methodology of internal model control (IMC) with anti-windup feature is suggested. Due to presence of integral action in the conventional IMC structure reset windup effect is found quite often. This limitation can be overcome by incorporating the proposed anti-windup scheme. Efficacy of the reported technique is experimentally verified on hot air flow process manufactured by Quanser (2014) and level control loop by Feedback (2010).
  Keywords: internal model control; integral windup; anti-windup scheme; model identification; hot air flow process; level control loop.
  
  
• A new Analytical Approach for Phase-margin Specification based Target-loop Selection for different class of Dead-time Processes  
  by Sudipta Chakraborty 
  Abstract: Target-loop based controller design is one of the most utilized strategy in industry. For such designs, selection of the proper target-loop is the most important task. In this paper, an analytical approach for selection of target-loop for different classes of processes have been presented. Explicit formulas for the target-loop selection have been derived with phase margin specifications. To validate the proposed target-loop, case studies on various process models have been made. Lastly, to check the applicability and superiority, a control comparison with proposed loop based controller and a recently developed one is also included.
  Keywords: Time-delay; Target-loop; Phase-margin; Robustness; Maximum sensitivity; Loop-shaping based controller design; PID Control; Loop-shaping.
  
  
• Control of the rated production power of DFIG-wind turbine using adaptive PSO and PI conventional controllers  
  by Hazem Hassan Ali, Ghada Saeed Elbasuony, Nashwa Ahmad Kamal 
  Abstract: Production of rated power and protection of generator and power converter from an overload are necessary. Therefore; measuring the accurate value of the pitch angle of Doubly Fed Induction Generator (DFIG) wind turbine is essential. An assessment study between the adaptive Particle Swarm Optimization (PSO) technique and conventional Proportional Integral (PI) controller of the pitch control system in limiting the electrical output power at the rated value of DFIG is introduced in this study. Pitch control with PSO is designed by solving the nonlinear equation of pitch angle at each wind speed higher than rated wind speed. The PI controller gains of the pitch system are evaluated to keep the power limited at rated value. Accuracy in measuring pitch angle is essential because a small difference in pitch angle value results in an overload on the generator. The performance of each parameter of DFIG with a detailed analysis is studied. The simulation shows that the pitch control system with PSO technique gives better results in regulating the DFIG output power compared to PI controller method under wind speed variation.
  Keywords: DFIG wind turbine; pitch controller; optimization; PSO; conventional controller.
  
  
• Residual based Fault Detection Isolation and Recovery of Greenhouse  
  by Rahul Singhal, Rajesh Kumar, Satyanarayana Neeli 
  Abstract: This paper is concerned with fault detection, isolation and recovery (FDIR) of the greenhouse whose temperature is regulated by the model predictive controller (MPC). The residual generation approach is adopted for fault detection and isolation. The new considerations in the proposed FDIR approach are the residual generation for actuator faults, regulation failure detection as the indication of inappropriate regulation by the controller, below threshold actuator fault detection strategy, and recovery operation updating model used by MPC once the FDIR isolates the actuator fault. The proposed control strategy FDIR with MPC was compared with fixed model information MPC for simulated scenarios of the actuator fault. It has been shown that FDIR successfully detects, isolates and updates model information with low computation burden for non-delayed control evaluation.
  Keywords: Fault detection; fault isolation; fault recovery; greenhouse environment control; model predictive control.
  
  
• A Multi-Objective Criterion and Stability Analysis for Neural Adaptive Control of Nonlinear MIMO Systems: An experimental validation  
  by Asma Atig, Fabrice Druaux, Dimitri Lefebvre, Ridha Ben Abdennour 
  Abstract: This paper presents a multi-objective indirect neural adaptive control design for nonlinear square multi-variable systems with unknown dynamics. The control scheme is made of an adaptive instantaneous Neural Emulator, a Neural Controller based on fully connected Real-Time Recurrent Learning RTRL networks and an online parameter updating law. A multi-objective criterion that takes into account the minimization of the control energy is considered. The contribution of this paper is to develop a new controller parameter optimization based on the Lyapunov stability analysis while ensuring control issues with environmental and economical objectives. Performance of the proposed approach in terms of regulation, tracking and minimization of the control energy is evaluated by numerical simulations of a disturbed nonlinear multi-variable system. The obtained control scheme is then applied in real time to a disturbed MIMO thermal process.
  Keywords: Multi-objective approach; Adaptive control; Stability analysis; Recurrent Neural Networks; Nonlinear systems; MIMO processes.
  DOI: 10.1504/IJAAC.2022.10038785
   
  
• Design and Optimization of a Fuzzy-PI Controlled Modified Inverter based PMSM Drive Employed in Light Weight Electric Vehicle  
  by Chiranjit Sain, Atanu Banerjee, Pabitra Kumar Biswas, Ahmad Taher Azar 
  Abstract: In this paper a PWM (Pulse Width Modulation) controlled permanent magnet synchronous motor (PMSM) is designed and illustrated using fuzzy logic control. The traditional PI controller is modified as a fuzzy-PI controller for faster dynamic response. The fuzzy logic operated speed controller ensures an almost zero steady-state speed error in this proposed approach. In addition, a stochastic optimization tool such as the updated particle swarm optimization (PSO) technique is used to optimize the performance of the proposed fuzzy logic controller. Further, the armature current response is sinusoidal in a sinusoidal PWM control strategy. The pulse width modulated buck-converter fed voltage source inverter (VSI) acts as a current source inverter (CSI) and is remarkably efficient in minimizing the voltage and current ripples compared to the traditional VSI. The economical as well as robust design procedure for the buck converter fed VSI is also proposed. With the configuration of the inverter, an improved over-current protection method is developed for the permanent magnet motor in a real-time light electric vehicle. Later, the control strategy of the developed prototype is tested and verified using a low-cost microcontroller. Furthermore, to improve the robustness of the drive, sensorless speed estimation procedure is also adopted in the real-time set-up. Finally, simulations and test results reveal the efficacy of this proposed approach over the existing control topologies.
  Keywords: Electric vehicle; Fuzzy control; Optimization; PWM; PMSM.
  DOI: 10.1504/IJAAC.2022.10038131
   
  
• A virtual GPS design using information of indoor localization system for robotics navigation  
  by Mohammad Hayajneh, Antonio Galiano 
  Abstract: For external robot platforms under development, people must perform repeated experiments to adjust the gains, log data and check performance. Therefore, carrying out such experiments outdoor, especially those requiring position information using Global Positioning System (GPS), could be inconvenient for researchers in this stage. Furthermore, Testing robots such as drones would increase the risk on people lives because of abnormal behaviors from non-proved approach. Therefore, the completion of experiments in the laboratory will be safer and faster, but it is known that the GPS receivers used in robots do not operate efficiently inside buildings. For this reason, this work has adopted a novel design of virtual GPS that calculate its data through an indoor motion tracking system. Basically, the approach of this work converts the position information provided by the motion tracking system in local frame into the corresponding global positions in geodetic coordinates. To maintain the actual connection between GPS and the robot, the location data set is formatted in specific binary structure using UBX messages and is provided through a serial connection to the GPS port in the robot side. To validate the adopted approach, real-time executions were performed on a powerful Linux computer that handles the computational cost of complex algorithms, provides serial communication with a quadcopter, and establishes the wireless communication with a ground station.
  Keywords: virtual-GPS; Quadcopter; Optitrack; UBX; Pixhawk; Raspberry pi; Navigation.
  
  
• Stable Optimal Self-tuning Interval Type-2 Fuzzy Controller for Servo Position Control System.  
  by Ritu Rani De (Maity), Rajani K. Mudi, Chanchal Dey 
  Abstract: An optimal self-tuning interval Type-2 fuzzy controller for servo position control systems is reported here. To achieve precise positioning of the actuator, input scaling factors of an interval Type-2 fuzzy proportional-integral controller are updated online depending on the latest operating conditions in terms of closed loop tracking error and change of error. To ensure desired performance, input scaling factors are obtained by adaptive cuckoo search based optimization algorithm. Efficacy of the proposed scheme is substantiated through performance comparison with recently reported peak observer based and online self-tuning based interval Type-2 fuzzy PID, interval Type-2 fuzzy PI, and also Type-1 fuzzy PI controllers through simulation study along with real-time validation on a DC servo position control system. Lyapunov function based stability analysis for the proposed controller is also provided.
  Keywords: Type-2 fuzzy control; self-tuning mechanism; optimal fuzzy control; servo position control; real-time experimentation.
  
  
• Closed-loop thickness control and sensor placement in extrusion blow molding  
  by Mostafa Darabi, Raffi Toukhtarian, Hossein Vahid Alizadeh, Benoit Boulet 
  Abstract: Extrusion Blow Molding (EBM) is a polymer forming technique used in manufacturing processes which produce hollow plastic parts such as fuel tanks for the automotive industry. Mathematical models built using the Finite Element Method (FEM) and simulation-based optimization of the extrusion process are studied extensively in the literature to improve the quality of the final parts as well as to minimize material usage. Such optimization techniques are open-loop tasks that obtain the optimal die gap setpoints for the next cycles of the EBM process. When new feed material is introduced or an unexpected machine drift occurs, a trial and error method is used to retune the die gap setpoints, which is time consuming and costly. Therefore, in this work, the feasibility of using a closed-loop control approach in the EBM process is studied to compensate for machine drift and disturbances in real time. An automatic control system for regulating the extrusion process in EBM is proposed. The controller aims to increase the consistency of the manufacturing process and minimize machine drift. The thickness of the extrudate is measured in real time and any unexpected drift is compensated for by changing the die gap instantly. The model of the closed-loop system used in this work was developed in previous work and has a transport Partial Differential Equation/ Non-linear Ordinary Differential Equation cascade structure model. The controller has a Smith predictor configuration to compensate for the input-dependent delay generated by the polymer transport partial differential equation. In addition, H1 control theory is used to design an optimal controller in order to maintain the desired extrudate thickness in the presence of disturbances or machine drift. A simulation of the H1 controller system shows better thickness accuracy under upset condition compared to the open loop system. This new control technique may reduce the useless of scrapped parts and improve product quality.
  Keywords: H infinity controller; Optimal control; Smith predictor; Parameter identification; Polymer extrusion; Sensor placement; Variable die gap; Input-dependent delay; Hammerstein block.
  
  
• Robust Attitude Stabilization Reactive Control of Quadruped Robot under Load Disturbance  
  by Xiaolu Zhu, Jinxiao Wan, Wei Xu, Chuan Zhou 
  Abstract: In order to solve the walking stability problem of quadruped robot under load disturbance, a robust attitude stabilization reactive control strategy based on ZMP (zero-moment point) stability criterion is proposed. This control strategy is composed of two layers, including an adaptive gait planner based on ZMP criterion at the upper planning layer and a robust joint angle tracking controller at the bottom layer. The adaptive gait planner calculates the desired ZMP position in real time by the robot body dynamics, which can adaptively plan the foot-end trajectory of the quadruped robot to ensure the attitude stability of the robot under load disturbance. The robust joint angle tracking controller is a sliding-mode tracking controller with disturbance observer, which calculates the disturbance and compensates for it through the complete dynamics of the quadruped robot with disturbance term. The robust joint angle tracking controller can enable quadruped robot to maintain robust joint angle tracking under the load disturbance. Finally, the effectiveness of the proposed reactive control strategy is verified by co-simulation of ADAMS and MATLAB.
  Keywords: quadruped robot; load disturbance; zero-moment point (ZMP); disturbance observer; sliding-mode control; reactive control.
  
  
• Load following approach for a VVER Nuclear Power Plant using Generic Model Control  
  by Khaled Rady, Mahmoud El Metwally, Ahmed Abouelsoud, Mahmoud Farrag, Said Kotb 
  Abstract: Possible application of generic nonlinear model control (GMC) for Dabaa pressurized water reactor (PWR) type nuclear power plant is studied for the load following operations. A self-adapting controller based on a generic minimal variance strategy is utilized. The controller design is modelled as a linear second-order model with unspecified time-varying parameters. Configuration parameters are modified online using an extended least square approach with a recursive prediction sequence; the controller is implemented on an optimized model from the literature. The cooling temperature is kept as close as possible to the desired value in the load following operating mode. The position of the control rod is selected as the control variable. The proposed controller was benchmarked against the conventional rod speed program controller (CRSP). The outcomes are promising and indicate the opportunity to implement the GMC approach for an actual nuclear power plant.
  Keywords: Generic nonlinear model control ; nuclear power plant ; Robust Controller.
  
  
• A Decoupler Assisted Optimized Controller Design for Greenhouse System  
  by SHRIJI GANDHI, Ravi Gandhi, Santosh Kakad, Manish Thakker 
  Abstract: This paper presents the application of the feedback feed-forward linearization integrated with the optimized PID controller for the greenhouse system (GHS). The GHS is a group of MIMO systems with highly coupled, disturbed, and nonlinear dynamics. Using the proposed approach, initially, the nonlinear MIMO system is separated into two independent SISO systems (i.e., temperature loop and humidity loop). After the decoupling, the separated loops are regulated by the Genetic Algorithm based optimized PID controllers. The extensive simulation study is carried out for the stabilizing and the tracking control operations for both the loops of the GHS in the presence of various initial conditions, various load disturbances (e.g. Canopy temperature, external temperature, external humidity, and solar radiation). Finally, the performance robustness of the proposed control structure is compared with the linear quadratic regulator(LQR) controller, conventional PID controller, and the Ziegler-Nichols tuned PID controller under diversified scenarios.
  Keywords: Feedback feed-forward linearization; Greenhouse system; PID controller; Genetic Algorithm.
  
  
• Novel Design of PID Controllers for Minimum and Non-Minimum Phase Time Delay Processes for Enhanced Performance  
  by Chandra Mohan Goud Ediga, Chidambaram M, Seshagiri Rao A 
  Abstract: A new technique is proposed for the design of PID controllers for minimum and non-minimum phase (NMP) stable first-order and second-order processes. The idea is to make the open-loop transfer function model equal to an integrating model with which the closed loop consists of first order dynamics. The resulting controller settings are functions of the tuning factor ? and process model parameters. The tuning parameter is selected based on the stability and robustness margins. Different case studies for simulation and experimental implementation are considered and the closed loop responses are examined. The proposed technique gives enhanced closed-loop performance for both set point and regulatory problems compared to the current techniques in both perfect and perturbed model conditions. Performance metrics such as integral error, control variation, stability margins, and sensitivity peak are compared and noted that the enhanced responses are achieved with the proposed simple method which has straight forward simple analytical relations for design of the controller.
  Keywords: FOPTD process; SOPTD process; PID control; Total variation; Maximum sensitivity.
  
  
• Adaptive differential evolution with linear population reduction for parameter estimation of solar cell models  
  by Zhen Yan, Wenyin Gong, Shuijia Li 
  Abstract: Parameter estimation of solar cell models is an important part of photovoltaic power generation system. However, it is still a challenging problem. In this study, an adaptive differential evolution with linear population reduction, called LRJADE, is developed to accurately estimate solar cell models parameters. In LRJADE, the linear population reduction strategy is employed to accelerate convergence speed. Additionally, the crossover rate repairing is also used. The performance of proposed LRJADE is verified by 13 benchmark functions and two solar cell model parameter estimation problems. Simulated results show that LRJADE not only obtains promising results in benchmark functions, but also achieves the very accurate solutions to solar cell model parameter estimation problems.
  Keywords: Parameter estimation; solar cell models; differential evolution; adaptation.
  
  
• Swarm Activity-Based Dynamic PSO for Distribution Decision  
  by Yu Su, Lingjuan Hou 
  Abstract: The distribution decision is a complicated constrained optimization problem that plays a key role in the production planning and inventory scheduling at the current era of intelligent big data, since few studies have developed new models integrating intelligent supply chain management. Aim at the limitation of traditional methods which are difficult to obtain feasible solutions in large-scale search space with limited time, a new swarm activity-based intelligent optimization algorithm, called PSO-SAW, is reconstructed in this paper by improving particle swarm optimization (PSO). The methodology is validated through several benchmarks and experimental applications to some distribution decision problems adopted from the literatures. Empirical results have implied the feasibility, effectiveness and robustness of the proposed method. Moreover, the experimental results of the algorithm have also verified the promising performances and applicability to distribution decision problems by comparing with other considered stochastic algorithms.
  Keywords: particle swarm; swarm activity; dynamic inertia weight; distribution decision; supply chain optimization.
  
  
• Intelligent Optimal Hybrid Motion/Force Control of Constrained Robot Manipulator  
  by Komal Rani, Naveen Kumar 
  Abstract: This paper presents a neural network based optimal control approach for hybrid motion/force control problem of robot manipulators in the presence of structured and unstructured uncertainties. The quadratic optimization with sliding mode control and neural network is utilized to propose an optimal hybrid motion/force control of robotic manipulators. Firstly, the dynamics of robot manipulator is reduced into state-space form describing the constrained and unconstrained motion separately. Then the optimal hybrid motion/force control scheme is derived utilizing the optimization of Hamilton Jacobi Bellman (HJB) equation and sliding mode control approach. The structured and unstructured uncertainties of the system are compensated using radial basis function neural network and adaptive compensator. The radial basis function neural network approximates the unknown dynamics and adaptive compensator is used to estimate the bounds on neural network approximation error and the unstructured uncertainties of the system. The neural networks are trained in online manner using weight update algorithms derived with Lyapunov approach to guarantee the tracking stability and error convergence with prescribed quadratic performance index. Finally, the proposed control approach are verified through simulation results with two link constrained manipulator in a comparative manner.
  Keywords: Optimal hybrid Motion/force control; Sliding mode control; Neuralrnnetwork; Constrained and unconstrained motion; Hamilton Jacobi Bellman (HJB)rnoptimization; performance index.
  
  
• Tuning of extended Kalman filter using grey wolf optimisation 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 optimisation (GWO) for sensor less speed control of permanent magnet synchronous motor (PMSM) drive. A real-coded GWO is used to optimise the noise covariance matrices of EKF in an off-line manner. The optimised 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 optimisation (PSO) method.
  Keywords: extended Kalman filter; EKF; grey wolf optimisation; GWO; particle swarm optimisation; PSO; permanent magnet synchronous motor; PMSM; PI controller.
  DOI: 10.1504/IJAAC.2021.10037939
   
  
• Online map fusion system based on sparse point-cloud  
  by Shiqin Sun, Benlian Xu 
  Abstract: With the gradual maturity of single-robot simultaneous localisation 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 centralised 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, optimisation 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: simultaneous localisation and mapping; SLAM; map building; robot navigation; online map fusion.
  DOI: 10.1504/IJAAC.2021.10037940
   
  
• 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; MPC; fractional-order system; multi-model system; nonlinear system; singularity function approximation; conical tank system.
  DOI: 10.1504/IJAAC.2021.10037942
   
  
• Suppression of chaos in the spin-orbit problem of Enceladus via robust adaptive sliding mode control  
  by Israr Ahmad, A. Othman Almatroud, M. Mossa Al-Sawalha 
  Abstract: This paper proposes a new robust adaptive sliding mode control (RASMC) technique and investigates the control of chaos in the two-dimensional uncertain spin-orbit problem of Enceladus (SOPE) in the presence of external disturbances and model uncertainties. The external disturbances, model uncertainties, and nonlinear terms of the system are bounded and unknown. The proposed RASMC technique: 1) accomplishes oscillation free and fast convergence of the state variables to the origin; 2) suppresses chattering in the control inputs. The Lyapunov stability theory verifies the convergence behaviour and guarantees the robust asymptotic stability of the equilibrium point at the origin. In the sense of Lyapunov function, this article also provides parameters adaptation laws that confirm the convergence of uncertain parameters to some constants. The computer simulation results verify the theoretical findings and provide comparative analysis. The results of this study could be beneficial in the area of space sciences.
  Keywords: control of chaos; Lyapunov stability theory; robust adaptive sliding mode control; RASMC; spin-orbit problem of Enceladus; SOPE; uncertain parameters.
  DOI: 10.1504/IJAAC.2021.10037946
   
  

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

• Design of a PV module block using the industrial automation PLC for PV system application  
  by Youness Ouberri, Hanane Yatimi, Elhassan Aroudam 
  Abstract: The photovoltaic (PV) module parameters extraction for the PV modelling 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 modelling using the single diode model. The main contributions of this work are: a) geometrical PV cell parameters extraction; b) PV modelling 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 modelling using automation software, the current-voltage (I-V) curves and the power-voltage (P-V) curves for several irradiation levels at 25°C will be plotted and evaluated. The characteristic curves are plotted using the automation human-machine interface (PLC-HMI). The results reveal that the automation PLC through the PV block presents a very good modelling of the PV module that can be used as an input for any PV system.
  Keywords: PV modelling; parameters extraction; automation PLC; HMI.
  DOI: 10.1504/IJAAC.2021.10037937
   
  
• Binary particle swarm optimisation and the extreme learning machine for diagnosing paraquat-poisoned patients  
  by Xuehua Zhao, Xin Tian, Zhen Li, Xu Tan, Qian Zhang, Huiling Chen, Lufeng Hu, Shuangyin Liu 
  Abstract: The diagnosis of paraquat-poisoned patients is one of the important problems in the medical diagnosis field. Current methods identify the paraquat-poisoned patients mainly depending on paraquat content in the body. However, the lack of such methods is treating paraquat-poisoned patients as a healthy person when there is little paraquat content in the body. Here, a new diagnostic method for paraquat-poisoned patients is proposed, which fuses gas chromatography-mass spectrometry, binary particle swarm optimisation and extreme learning machine together. In the proposed method, the data is collected by gas chromatography-mass spectrometry, the binary particle swarm optimisation is adopted to select the excellent feature sets and the extreme learning machine is adopted to identify the paraquat-poisoned patients. In contrast to current methods, the proposed method still can accurately identify the paraquat-poisoned patients even if there is little paraquat content in the body. In our experiments, two measures, which are accuracy and sensitivity, are used to evaluate our method. The accuracy and sensitivity get to 93.90% and 94.54%, respectively. We also made comparisons with four algorithms and the experimental results show that our method has better performance than the other four methods.
  Keywords: medical diagnosis; paraquat-poisoned patients; feature selection; extreme learning machine; ELM; particle swarm optimisation; PSO.
  DOI: 10.1504/IJAAC.2021.10037935
   
  
• 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, Zhenping Li 
  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 an electric vehicle routing problem with soft time windows and fast charging (EVRPSTW-FC) stations: 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; HAGA; vehicle routing problem; soft time windows.
  DOI: 10.1504/IJAAC.2021.10037936
   
  
• Optimised data-driven terminal iterative learning control based on neural network for distributed parameter systems  
  by Xisheng Dai, Lanlan Liu, Zhenping Deng 
  Abstract: In this paper, a data-driven iterative learning control with neural network-based optimisation 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 mapping relation from the desired output to the desired input, and the iterative learning algorithm of neural network weight is obtained by optimising the system performance indexes. Then, a detailed theoretical analysis based on composite energy function is given. Moreover, unlike traditional iterative learning control task tracking the whole trajectory, tracking time terminal is taken into account in this paper. Finally, simulation results show the feasibility of the theory.
  Keywords: data-driven control; iterative learning control; ILC; distributed parameter systems; DPSs; neural network; convergence.
  DOI: 10.1504/IJAAC.2021.10037938
   
  
• The impact of horizontal R&D cooperation on the climbing of the industrial cluster supply chain: from the perspective of the evolutionary game theory  
  by Juanli Lan, Shi Cheng, Bingxuan Wang, Hongzhen Lei 
  Abstract: Supply chain upgrade is a transformation process of supply chain operation efficiency and value realisation. The upgrading of an industrial cluster supply chain is of great practical significance to the development of China's cluster enterprises. As a kind of spatial economic organisation with geographical proximity and industrial relevance, supply chain network and industrial cluster create a favourable platform for enterprise cooperation. This paper studies the R&D cooperation of industrial clusters based on the perspective of the 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 realising the strategy and path of the supply chain climbing of China's industrial clusters.
  Keywords: industrial cluster; R&D cooperative; horizontal enterprise; supply chain climbing.
  DOI: 10.1504/IJAAC.2021.10037943
   
  
• Self-adaptive wolf pack algorithm based on dynamic population updating for continuous optimisation 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 optimisation problems as well as real-world optimisation 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 (SWPA-DU) strategy. First, the self-adaptive chaotic scouting behaviour 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; WPA; self-adaptive chaotic scouting behaviour; Cauchy perturbation operator; dynamic population updating.
  DOI: 10.1504/IJAAC.2021.10030290
   
  
• Study of braking strategy considering comfort  
  by Shenpei Zhou, Haoran Li, Qingyong Zhang, Meng Gao, Bin Ran 
  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 optimisation method of combining with driving comfort, braking distance and pedal force. Then the output of the model is analysed by memetic algorithm to make the braking distance smaller than the safety distance, and the driving comfort is optimised as well. The results of experiment show that the best solution of the model can satisfy the constraints 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 optimisation; memetic algorithm; driving comfort.
  DOI: 10.1504/IJAAC.2021.10037941
   
  

Special Issue on: Advances and Applications of Sliding Mode Control in Engineering

• Fuzzy Adaptive Finite-Time Sliding Mode Controller for Trajectory Tracking of Ship Course Systems with Mismatched Uncertainties  
  by Pooyan Alinaghi Hosseinabadi, Ali Soltani Sharif Abadi, Saad Mekhilef 
  Abstract: In this paper, a new finite-time robust controller is designed for the ship heading (course) control systems with unknown mismatched external disturbances and uncertainties to fulfil trajectory tracking task. The adaptive method is utilized to estimate the upper bound of these mismatched disturbances and uncertainties. Meanwhile, the fuzzy logic is employed to enhance the tracking performance. In short, the fuzzy controller, adaptive, finite-time stability concepts and Sliding Mode Control (SMC) scheme have been incorporated to propose Fuzzy Adaptive Finite-time SMC (FAFSMC) scheme to utilize their benefits and to compensate the shortages of applying them individually. The finite-time stability analysis is investigated by utilizing the Lyapunov stability theory. The simulation results are carried out for the proposed fuzzy scheme and five other existing non-fuzzy schemes including Simple, Classical, Cubic, Hexagonal, and Switching to reveal the effectiveness of the proposed scheme compared to the other five schemes.
  Keywords: fuzzy; sliding mode control; finite-time; adaptive; robust; Lyapunov.
  
  
• Nonsingular terminal sliding mode control for a class of second-order systems with mismatched uncertainty  
  by Ning Zhao, Ye Zhang, Xincheng Yang, Dongya Zhao 
  Abstract: In this study, a novel method is proposed for a class of second-order systems to solve singularity problem and mismatched uncertainty in terminal sliding mode control (TSMC). Firstly, a state transformation method is designed to eliminate the reaching phase of terminal sliding mode, which can avoid the singularity problem fundamentally. In addition, mismatched uncertainty can be transferred to the same channel with control input after state transformation. Secondly, a novel controller is designed to stabilize the new form of the system in finite-time, while the mismatched uncertainty can be compensated effectively. Numerical simulations further validate the effectiveness of the proposed method.
  Keywords: terminal sliding mode; second-order systems; singularity; mismatched uncertainty; state transformation matrix.
  
  
• Adaptive sliding mode controller design for the bipartite consensus tracking of multi-agent systems with actuator faults and disturbances  
  by Ehsan Nazemorroaya, Mahnaz Hashemi 
  Abstract: This paper studies the bipartite consensus problem for second-order nonlinear multi-agent systems in the presence of actuator faults, unknown control gains and unknown external disturbances. The actuator faults are considered as partial loss of effectiveness fault and bias fault. For design controller, the control gains and disturbances only need to have unknown upper bounds. Also a signed bipartite directed graph is used for describing the communication topology of the multi-agent system. An adaptive sliding mode controller is developed for bipartite consensus tracking of the multi agent system. The proposed adaptive sliding mode controller ensures the uniformly ultimately bounded cooperative tracking of the multi-agent system. Finally, the correctness and effectiveness of the proposed control method is verified via simulation results.
  Keywords: Actuator fault; Bipartite consensus; Adaptive sliding mode control; Uniformly ultimately bounded.
  
  
• A Relative Analysis of Sliding Mode Control with Reaching Law for the Vector Control of Three Phase Induction Machine  
  by Jisha Kuruvilla P, Anasraj Robert 
  Abstract: Induction motor constitutes an established test bed in the automatic control theory frame-structure as it is a higher order, multiple input- output, nonlinear system operating under uncertainty conditions. The Sliding Mode Control (SMC) method is a well-developed, promising control procedure appropriate for non-linear, time-dependent, uncertain systems. Due to its dynamic nature and robustness towards uncertainties, SMC is a most suggested technique for implementing in induction motor drives. This paper focuses on implementing various continuous- time, SMC strategies characterized by reaching lawapproach in an induction motor drive. The paper also proposes a comparative analysis of various continuous-time reaching-laws on the performance of the motor towards parameter and load variations and the chattering issues.
  Keywords: Sliding Mode control; Reaching Law;constant reaching law;proportional reaching law;,power rate reaching law;exponential reaching law;enhanced exponential reaching law; Vector control; Motor drives.
  
  
• A new form of a class of MIMO linear systems for nonsingular terminal sliding mode control  
  by Ning Zhao, Shouli Gao, Yuankai Song, Dongya Zhao 
  Abstract: In this study, a novel method is proposed for a class of MIMO linear systems nonsingular terminal sliding mode control to solve singularity problem. A state transformation matrix is designed properly to transfer a class of MIMO linear systems into a new form without changing its controllability and observability, the design method and application conditions are given. Based on it, the traditional nonsingular method of second-order nonlinear systems can be used in the new form of MIMO linear systems by adjusting the dimension of coefficients appropriately. Then an example is given to validate the effectiveness of the proposed form. Stability analysis and numerical simulations show that the proposed method can guarantee system states to converge to equilibrium point with strong robustness in finite time, and the singularity problem can also be avoided appropriately.
  Keywords: Terminal sliding mode; Nonsingular; MIMO linear system; State transformation matrix.
  
  
• Power Control of a Stand-Alone Electric Generation Hybrid System using Integral Sliding Mode Controller  
  by Fatima Ez-Zahra Lamzouri, El-Mahjoub Boufounas, Aumeur El Amrani 
  Abstract: This paper proposes a novel strategy for output power control of an electric generation hybrid system (EGHS), composed of a photovoltaic generation system, a storage battery bank and a variable load. According to different atmospheric conditions and load changes, a robust control based on sliding mode control (SMC) is designed to satisfy the total power demand in different power system operation modes. Thus, the proposed controller is modified by introducing the integral action in the switching surface, in order to improve transient response with minimum steady state error. Numerical simulations are presented and discussed to demonstrate the performance of the proposed method, using a nonlinear model of the plant. Finally, the simulation results show that the proposed Integral SMC (ISMC) strategy ensures better response speed and smaller steady-state error compared to standard SMC.
  Keywords: hybrid power generation system; maximum power point tracking; power control; integral sliding mode control.
  
  
• Fixed-Time Sliding Mode Flight Control with Model-Based Switching Functions of Quadrotor Unmanned Aerial Vehicles  
  by Charles Fallaha, Yassine Kali, Maarouf Saad, Jawhar Ghommam 
  Abstract: This paper proposes the design of a new sliding mode controller of the attitude fast inner-loop of a drone quadrotor type system. The controller uses the novel model-based switching functions approach, which leads to important simplifications of the pitch, roll and yaw torques control inputs fed to the quadrotor. The model-based switching functions approach forces as well a complete chattering decoupling of these three torque inputs and enhances the robustness of the closed-loop system. The proposed approach is combined with the fixed-time sliding mode approach, and is experimentally implemented and successfully validated on a quadrotor system.
  Keywords: Sliding Mode Control; Model-Based Switching Functions; Fixed-Time Sliding Mode; Quadrotor; UAV.
  
  
• Modified Sliding Mode Control for Universal Active Filter based Solar Microgrid System  
  by Buddhadeva Sahoo, Sangramkeshari Routray, Pravat Kumar Rout 
  Abstract: This manuscript presents a larger signal model by combining the universal active filter (UAF) and the solar system with a vector switching operation (VSO) in a novel frame. The detailed modeling of the S-UAF using VSO and modified sliding mode control (MSMC) is proposed for achieving better power quality (PQ) operation. MSMC based novel frame is used to estimate the accurate reference signals for SHAF during dynamic state conditions such as sag/swell, change in irradiance, fault and sensitive load condition. SEAF control is based upon the conventional dq control strategies and it adjusts the load voltage during dynamic conditions. SHAF is used to balance the grid side current and reduces the harmonic distortion by injecting the appropriate current in quadrature with the load current and facilitates fast transient response during sudden load change by providing better tracking capability and reduction in the switching losses. To validate the proposed controller and S-UAF approach with different test conditions, it is tested in Matlab/Simulink environment and the related results are discussed.
  Keywords: Vector switching operation (VSO); modified sliding mode controller (MSMC); universal active filter (UAF); dynamic nonlinear transformation (DNT); Power quality (PQ).
  
  

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.
  
  
• Scheduling Problems with Rejection and Piece-rate Maintenance to Minimize the Total Weighted Completion Time  
  by Xianyu Yu, Zhen Wang, Kai Huang, Dehua Xu, Xiuzhi Sang 
  Abstract: This paper addresses the single machine scheduling problems with simultaneous consideration of rejection and piece-rate maintenance. Each job is either accepted to be processed on the machine, or rejected in which case a rejection penalty will be incurred. The piece-rate maintenance refers that the machine performs maintenance activity every time it completes a given number of jobs. The objective is to minimize the sum of weighted completion times, rejection costs and maintenance costs. Our contribution is threefold. First, the general case of the considered problem is proved to be NP-hard, and an approximate algorithm is developed to solve the problem. Second, for the case with agreeable condition that jobs with smaller processing times are weighted more, a pseudo-polynomial algorithm is developed to establish that the problem is NP-hard only in the ordinary sense. This pseudo-polynomial algorithm is further converted into a fully polynomial time approximation scheme (FPTAS). In the third, two special cases, in which one with all equal weights and the other one with all equal processing times, are proved to be solved in polynomial time.
  Keywords: Scheduling; Rejection; Maintenance; Agreeable Condition; FPTAS.
  
  
• Research on Steelmaking-Continuous Casting Production Scheduling Problem with Uncertain Processing Time Based on Lagrangian Relaxation Framework  
  by Liangliang Sun, Zhi Li, Ye Li, Yaqian Yu, Jinyu Liu 
  Abstract: Abstract: Considering the problem of uncertain processing time in steelmaking-continuous casting, disrupting the dynamic balance of logistics and time, aiming at the analysis and description of uncertain processing time in steelmaking-continuous casting scheduling, a Markov chain transfer matrix is established to accurately simulate the uncertain processing time probability. For traditional Lagrangian relaxation algorithm for steelmaking-continuous casting production scheduling solution when as a result of each iteration should be accurate to solve the problem of low efficiency, design do not need to estimate the optimal value, based on the gradient direction control Surrogate Subgradient algorithm of iterative optimization strategy, to guarantee the quality of the premise algorithm to improve the algorithm convergence speed and efficiency. Finally, the method proposed in this paper is feasible and effective through the practical certification of large steelmaking plants.
  Keywords: steel-continuous casting; scheduling; the processing time; uncertainty; the Markov chain transfer matrix; Lagrange relaxation algorithm; gradient direction; Surrogate Subgradient algorithm; iterative optimization strategy; the algorithm convergence speed.