International Journal of Automation and Control (49 papers in press)

Regular Issues

• Synthesize of MPC Controller for Uncertain Systems subject to Input and Output Constraints: Application to Anthropomorphic Robot Arm  
  by Imen Dakhli, Elyes Maherzi, Mongi Besbes 
  Abstract: This paper proposes a synthesis of a dynamic controller under constraints. It is based on Model Predictive Control (MPC) approach and resolution of a convex optimization problem with Linear Matrix Inequalities (LMI). The controller guarantees the closed-loop stability for polytopic time-varying uncertain systems. Conditions are provided for the controller design based on the parameter dependent Lyapunov functions (PDLF). A new demonstration is developed based on the relaxation technique, to include a slack variables Gi. The new LMI's formulation offers an additional degree of freedom for the controller design. Input and output constraints are also taken into account during the design of the controller. This approach allows varying and adjusting the dynamic of system by taking into account input/output constraints.
  Keywords: Model Predictive Control (MPC); Dynamic controller; Linear matrix inequality (LMI); Parameter dependent Lyapunov functions (PDLF); Input / Output Constraint.
  
  
• Optimal Preview Control Design of Pneumatic Servo System: A Comparative Analysis  
  by Randeep Kaur, Jyoti Ohri 
  Abstract: This paper describes the development of a preview controller based on optimal control theory for positioning control of a pneumatic servo system. The preview theory of tracking control using the future information has been studied extensively but did not get enough applications in the field of servo control systems, used in modern industrial processing, that will increase the accuracy and efficiency of the industrial product. In the pneumatic servo system, the position of pneumatic cylinder is the command input and its future information can be known a priori. The proposed control system is composed of feedback from the state of the plant and feed-forward from the reference signal by using future information. The validity of the proposed control system is confirmed by simulation and comparative analysis is done with optimal control. Simulation results imply that good performance and good phase characteristics can be obtained using optimal preview controller.
  Keywords: Pneumatic Servosystem; Preview Control; Optimal Control; Algebraic Riccati Equation; Performance Index; Tracking.
  
  
• Stabilisation of a rotary inverted pendulum system with double-PID and LQR control: Experimental verification  
  by Teng Fong Tang, Shin Horng Chong, Kee Kiat Pang 
  Abstract: Rotary inverted pendulum (RIP) system is an under-actuated system. The RIP system consists of a pendulum, which is rotating freely in the vertical plane. A swing-up action using a pivot arm in the horizontal plane would then result in the pendulum to achieve upright equilibrium point. This paper describes the design of a double Proportional-Integral-Derivative (PID) controls with a Linear Quadratic Regulator (LQR) controller for the stabilisation control of a RIP system. The dynamic model of the RIP system is described too. The LQR controller was tuned using Taguchi method. The double-PID controller was designed using Ziegler-Nichols method, which the LQR controller is embedded in the RIP system to improve the stabilisation performance. The effectiveness of the double-PID and LQR controller is clarified with a RIP experimentally. The proposed controller has demonstrated succeed stable the pendulum within 0.5 degrees in 3 seconds and the rotary arm within 22.5 degrees.
  Keywords: rotary inverted pendulum; mathematical modelling; linear quadratic regulator; proportional-integral-derivative; Taguchi method; Ziegler-Nichols method.
  DOI: 10.1504/IJAAC.2020.10019766
   
  
• A Distributive Approach for Position Control of Clamps in a Reconfigurable Assembly Fixture  
  by Olayinka Olabanji, Khumbulani Mpofu, Olga Battaia 
  Abstract: Hydraulic actuator is a type of clamp used in a reconfigurable assembly fixture for exact positioning and effective immobilization of workpiece during the assembly process. However, due to their non-linearity, there is a need to design a control system for their effective performance. This study presents a distributive approach to mathematical modelling and position control of multiple hydraulic actuators used as a clamping system in a reconfigurable assembly fixture. The electrohydraulic system is verified experimentally in order to observe the synchronisation of the hydraulic actuators. The mathematical model of the system is developed in the Simulink environment. A Simulink model of the system is developed from the mathematical model and simulated with a Fuzzy-PID controller in order to obtain the response of all the actuators and other operating characteristics of the system. Simulation results are shown graphically in order to verify the theoretical development.
  Keywords: Electrohydraulic system; Position control; Fuzzy-PID controller; Hydraulic actuator; Reconfigurable Assembly Fixture; Distributive modelling.
  DOI: 10.1504/IJAAC.2020.10019826
   
  
• Stability Analysis and Robust Synchronization of Fractional-Order Modified Colpitts Oscillators  
  by Kammogne Soup Tewa Alain, Ahmad Taher Azar, Kengne Romanic, Fotsin Hilaire Bertrand 
  Abstract: Based on the stability theory of the fractional order system, the dynamic behaviours of the uncertain Colpitts oscillator with fractional order-derivative is studied. Furthermore, based on the extended bounded real lemma, the robust controller is obtained using the drive-response synchronization concept together with the Lyapunov stability theory formulated using the fractional Lyapunov direct method where the fractional-order q belongs to . In order to bring out the dynamic behaviour of this system, their phase portraits, the bifurcation diagrams and the Lyapunov exponent are simulated. Moreover, in this work, an approximated solution for both systems to show that the solution of such a system can be represented as a simple power-series function is provided. This study equally provides a systematic procedure to highlight the simplicity and flexibility of the suggested control approach. Simulations with both parameters uncertainty and external disturbance show the applicability and the efficiency of the proposed scheme.
  Keywords: Lyapunov exponent; Bifurcation diagram; Chaos; fractional modified Colpitts Oscillator; synchronization; robust controller.
  
  
• Novel Robust Stability Condition for Uncertain Systems with Interval Time-varying Delay and Nonlinear Perturbations  
  by Yubin Wu 
  Abstract: In this paper, the problem of robust stability analysis for a class of uncertain systems with interval time-varying delay and nonlinear perturbations is studied. In order to develop a less conservative stability condition, a LyapunovKrasovskii functional (LKF) comprising quadruple-integral term is introduced. A novel delay dependent stability criterion in terms of linear matrix inequalities (LMIs) is given by using a new delay-partitioning approach and reciprocally convex combination technique, which is derived by integral inequality approach (IIA). Compared with the existing literature, this criterion can greatly reduce the complexity of theoretical derivation and computation. Finally, three well-known numerical comparative examples are given to verify the superiority of the proposed approach in reducing the conservation of conclusion.
  Keywords: Interval time-varying delay; Delay-partitioning; Robust stability; Reciprocally convex combination; Lyapunov–Krasovskii functional (LKF); Quadruple-integral term.
  
  
• 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 Seshagiri Rao A 
  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.
  
  
• Adaptive wavelet network controller design for nonlinear time delay systems in the presence of actuator failure  
  by Mahshid Rahimifard, Marzieh Kamali, Maryam Zekri 
  Abstract: This paper presents an adaptive wavelet network controller for a class of strict-feedback uncertain nonlinear systems with unknown time delays and in the presence of external disturbances and actuator failure. The type of considered actuator failure is loss of effectiveness, in which the system input may lose unknown fraction of its effectiveness during the system operation. Wavelet networks are utilised to approximate unknown nonlinear functions and the proposed adaptive-neural controller is constructed based on dynamic surface control (DSC) design method. By applying the appropriate Lyapunov-Krasovskii functionals, the boundedness of all the closed-loop signals is guaranteed and the tracking error is proved to converge to a small neighbourhood of the origin. The performance of the proposed adaptive-neural control approach is illustrated by applying a theoretical system and a chemical reactor system. The simulation results indicate the effective capabilities of the proposed control algorithm.
  Keywords: adaptive neural control; wavelet networks; nonlinear time delay systems; dynamic surface control; DSC.
  DOI: 10.1504/IJAAC.2019.10021360
   
  
• H_∞ control of asynchronous networked control systems with Markov time delays  
  by Xiao-qiang Sun, Wei-jie Mao 
  Abstract: In this paper, the solutions to the H_∞ synthesis problems of stochastic asynchronous discrete-time (DT) networked control systems (NCSs) with random communication time delays are proposed according to the following steps. Firstly, a new Lyapunov-Krasovskii functional is explicitly constructed to analyse the stochastic stability of the systems. Both sensor-to-controller (S-C) and controller-to-actuator (C-A) random networked-induced delays, which are two Markov chains, are considered in the analysis. Moreover, the stochastic sampling of S-C and holding of C-A are also considered in the analysis. Secondly, based on a specified definition of the state with delay and a more rigorous inequality, sufficient delay-dependent stability conditions can be transformed into the form of linear matrix inequalities (LMIs), the LMIs can be conveniently solved by LMI tools. Finally, the proposed theory is validated by a simulation example.
  Keywords: H_∞ synthesis; DT asynchronous system; networked control; random delay; stochastic sampling and holding; delay-dependent LMIs; Markov chain.
  DOI: 10.1504/IJAAC.2019.10021362
   
  
• LQG controller design for a quadrotor UAV based on particle swarm optimisation  
  by Rabii Fessi, Soufiene Bouallègue 
  Abstract: This paper deals with the modelling and the linear quadratic Gaussian (LQG) control design of a quadrotor unmanned aerial vehicle (UAV) using different particle swarm optimisation (PSO) variants. Such a PSO-designed LQG controller is optimised in order to stabilise the position and the heading of the studied vertical take-off and landing (VTOL) quadrotor. Both canonical and recent variants of PSO algorithm, with linearly decreasing of inertia weight (PSO-In) and perturbed updating strategy (PSO-gbest), are considered for the systematically design and tuning of the LQG weighting matrices. These effective control parameters of the LQG approach represent the decision variables of the PSO-based LQG optimisation problem. Such an optimisation problem is formulated to minimise various performance time-domain criteria, like the integral of absolute error (IAE) and the maximum overshoot (MO) index, under nonlinear constraints related to the step responses of the closed-loop quadrotor dynamics. All proposed PSO algorithms are compared with each other and with the well known harmony search algorithm (HSA) and water cycle algorithm (WCA) metaheuristics for the stabilisation problem of the position and heading dynamics of the VTOL drone. Demonstrative simulation results are carried out in order to show the effectiveness of the proposed PSO variants-tuned LQG control approach.
  Keywords: quadrotor UAV; modelling; position and heading stabilisation; LQG weighting matrices tuning; particle swarm optimisation; PSO.
  DOI: 10.1504/IJAAC.2019.10021363
   
  
• Design and simulation of self-tuning fractional order fuzzy PID controller for robotic manipulator  
  by Reza Rouhi Ardeshiri, Hoda Nikkhah Kashani, Atikeh Reza-Ahrabi 
  Abstract: Two-link robotic manipulator system is completely nonlinear and time-varying multi-input-multiple output. In this research, the fractional order fuzzy PID (FOFPID) controller is proposed in order to control the robotic manipulator position. Since real control systems are generally nonlinear systems, therefore, better control of these systems requires the usage of an adaptive or nonlinear controller. So we applied a fuzzy system in order to determine the coefficients of a FOPID controller based on particle swarm optimization (PSO) algorithm. In definition of fitness function for this optimization, we considered integral of absolute error (IAE) and integral of absolute change in controller output (IACCO). Finally, in order to compare this controller with the FPID controller, numerical simulations were performed on the robotic manipulator. The results demonstrate that the overshoot of FOFPID controller is less than the FPID and proposed controller has less oscillations amplitude, totally, its performance is better than the FPID controller.
  Keywords: robotic manipulator; fractional calculus; FOFPID controller; fuzzy logic controller; PSO algorithm.
  DOI: 10.1504/IJAAC.2019.10021366
   
  
• PLC-based implementation of supervisory control for flexible manufacturing systems using theory of regions  
  by Sadok Rezig, Chekib Ghorbel, Zied Achour, Nidhal Rezg 
  Abstract: The supervisory control is a common theory for the synthesis of Petri net (PN) supervisors for discrete event systems given a PN model and a control specification for the maximum permissive behaviour. The theory of regions as one of control synthesis method generates a PN controller to satisfy the control specification. Though the theory of regions has for over a decade received a considerable attention in academy, still very few applications exist. The real cause of this seems to be a contradiction between the abstract controller and its physical implementation. This is evident in particular when the implementation is supposed to be based on a programmable logic controller (PLC), as is the case for flexible manufacturing systems. Indeed, since the synchronous PLC is based on signals, the PN supervisor remains asynchronous; this explains its implementation difficulty. In this work, a control synthesis method using the theory of regions is implemented with a Java application on PLCs of a flexible manufacturing system (FMS) installed in our research laboratory in the University of Lorraine in Metz, France.
  Keywords: Petri nets; signal interpreted Petri net; theory of regions; program logic controller; supervisory control; Petri net controller; reachability graph; algebraic methods; flexible manufacturing systems; FMSs; deadlock prevention.
  DOI: 10.1504/IJAAC.2019.10021365
   
  
• Hybrid intelligent controller design for an unstable electromagnetic levitation system: a fuzzy interpolative controller approach  
  by Ravi V. Gandhi, Dipak M. Adhyaru 
  Abstract: This article presents the design and implementation of hybrid intelligent controller for the dynamically nonlinear and unstable electromagnetic levitation system (EMLS). The hybrid design is having the intelligence of the fuzzy interpolative controller (FIC) with estimation ability and noise immunity achieved by means of the Kalman filter. The fuzzy inference system (FIS) is replaced by fuzzy linear interpolation networks based on look-up table to form the fuzzy rule base in ordered to reduce the computational complexity and to boost up the execution speed of the control approach as compared to conventional Mamdani or Sugeno type FIS toolkits. The proposed design stabilises the EMLS under wide initial and assorted operating conditions. Further, the proposed controller maintains the performance robustness under 0-25% of vertical payload disturbance by holding the steel ball within the safe limits. Simulation results are presented to validate the novelty and effectiveness of the proposed approach for EMLS.
  Keywords: fuzzy interpolative controller; FIC; Kalman filter; hybrid intelligent controller; electromagnetic levitation system; EMLS; set-point filter; stabilising control.
  DOI: 10.1504/IJAAC.2020.10019202
   
  
• Encoder-less field-oriented control of permanent magnet synchronous motor by using a full order adaptive state observer  
  by Sudhanwa Kelkar, B.B. Sharma 
  Abstract: This paper proposes a full order adaptive state observer, for the field-oriented control of a permanent magnet synchronous motor (PMSM), without using a speed sensor. The observer is designed in the estimated field-synchronous coordinate system. The rotor speed is treated as an unknown parameter. The adaptive law for speed is derived by using Lyapunov's stability theorem. Since control algorithms and models are processed in micro computers, the proposed observer has been discretised by Euler's method. To make the observer dynamically faster than the motor dynamics, observer poles are fixed proportionally to those of the motor by using pole placement technique. The simplifying assumptions used to discretise the observer, along with the stator current harmonics, introduce ripples in the estimated rotor speed. To mitigate this effect, various counter-measures are explored. The validity of proposed scheme is verified through MATLAB Simulink.
  Keywords: encoder-less; estimated field synchronous coordinate system; rotor speed; unknown parameter; MATLAB simulink; permanent magnet synchronous motor; PMSM; adaptive state observer; pole placement; Lyapunov's stability theorem; Euler's discretisation.
  DOI: 10.1504/IJAAC.2019.10022588
   
  

Special Issue on: Sliding Mode Control and Its Engineering Applications

• Adaptive terminal sliding mode control of high-order nonlinear systems  
  by Pooyan Alinaghi Hosseinabadi, Ali Soltani Sharif Abadi 
  Abstract: In recent decades, controller design has been attracted a great deal of interest of many researchers in control community which can make the job of many other researchers in different area of research easier. The present study aims to design a finite time adaptive control input for a high-order nonlinear system in presence of a variety of mismatched uncertainties and external disturbances. Adaptive terminal sliding mode control (ATSMC) method is used to design robust controller in a finite time. Also, adaptive concept is employed in ATSMC to estimate the upper bound of mismatched uncertainties and external disturbances and their estimations are used in control input. The finite time stability proof is performed by defining a proper candidate Lyapunov function. Numerical simulation results are carried out in Simulink/MATLAB to reveal the correctness of proposed design in this research. Finally, the performance criterion, integral of the square value (ISV), is defined to provide a numerical comparison between the proposed adaptive controller and non-adaptive controller.
  Keywords: adaptive; finite time; SMC; high order; terminal.
  DOI: 10.1504/IJAAC.2019.10022590
   
  
• Chaos control of a four-dimensional fundamental power system using pole placement-based proportional integral sliding mode control  
  by Manish Kumar, Piyush Pratap Singh 
  Abstract: In this paper, the problem of chaos control for a four-dimensional fundamental power system (FDFPS) model is investigated. Pole placement-based proportional integral sliding mode control (PISMC) is designed to control the chaos present in the system. Proportional integral sliding mode control law is derived by placing the poles at appropriate location to control the chaotic behaviour in four-dimensional fundamental power systems. The sufficient condition is derived for the asymptotic stability of the sliding manifold using Lyapunov stability theory. The proposed controller reduces the chattering, simplifies the design of power system stabiliser. Further the proposed pole placement-based PISMC is compared with conventional SMC approach. MATLAB is used for simulation. Simulation results show the effectiveness of proposed PISMC scheme.
  Keywords: chaotic system; chaos control; proportional-integral sliding mode control; SMIB power system; Lyapunov stability theory.
  DOI: 10.1504/IJAAC.2019.10022591
   
  
• Hybrid Petri network super twisting sliding mode control of wind turbine for maximum power point tracking  
  by Aghiles Ardjal, Rachid Mansouri, Maamar Bettayeb 
  Abstract: This paper deals with a hybrid sliding mode control and super twisting algorithm second order sliding mode control with Petri network (HSMC-STA) applied to reach the maximum power point tracking (MPPT) of a variable speed wind energy conversion system. It is aimed to solve the main and major undesired phenomenon faced by conventional sliding mode control, the high frequency oscillations (chattering), and to reduce the transient response (rise time) of super twisting algorithm second order sliding mode control systems. The design of a hybrid controller based on switching Petri network sliding mode control (PNSMC) is proposed, wherein a Petri network is used to supervise and switch between the classical sliding mode control law and the super twisting control law. The new hybrid controller is tested in a Simulink/MATLAB environment. Simulation results of the proposed control scheme present good dynamic and steady-state performance compared to the classical SMC and high order sliding mode with respect to the reduction of the chattering phenomenon and transient response.
  Keywords: sliding mode super twisting; robust control; Petri network; wind turbine; maximum power point tracking; MPPT.
  DOI: 10.1504/IJAAC.2019.10022592
   
  
• Super-twisting algorithm-based integral sliding mode control with composite nonlinear feedback control for magnetic levitation system  
  by Avadh Pati, Richa Negi 
  Abstract: The paper aims to discuss the issues of actuator saturation and external disturbance in the magnetic levitation (maglev) system. The proposed technique is composed of composite nonlinear feedback (CNF) and super-twisting algorithm (STA)-based integral sliding mode (ISM) control to tackle the problem of actuator saturation and external disturbances simultaneously. The composite nonlinear feedback scheme comprises of linear feedback law which provides stability and fast response whereas the nonlinear feedback law takes care of input saturation and reduces the overshoot. The super-twisting algorithm (STA)-based integral sliding mode (ISM) controller is designed for disturbance rejection. A super-twisting algorithm-based approach is applied on ISM scheme to eliminate the chattering effect and make it continuous in nature for its direct implementation to the physical maglev system. The designed scheme is successfully tested on real-time feedback instruments model of the maglev system.
  Keywords: maglev system; composite nonlinear feedback; CNF; super-twisting algorithm; STA; integral sliding mode; ISM; actuator saturation.
  DOI: 10.1504/IJAAC.2019.10022593
   
  
• A memristor-based system with hidden hyperchaotic attractors, its circuit design, synchronisation via integral sliding mode control and an application to voice encryption  
  by Sundarapandian Vaidyanathan, Ahmad Taher Azar, Akif Akgul, Chang-Hua Lien, Sezgin Kacar, Unal Cavusoglu 
  Abstract: A memristor-based system with hyperchaos and hidden attractors is introduced in this research work. The proposed four-dimensional memristor-based system exhibits both line equilibrium and no-equilibrium for different choice of parameters. An experimental emulation of the memristor-based system is carried out by an electronic circuit. An adaptive integral sliding mode controller is designed for globally synchronising a pair of memristor-based hyperchaotic systems with unknown parameters. As another application, the memristive system with hyperchaos is applied for voice encryption, which has potential applications in cryptosystems, computing and secure communication.
  Keywords: hyperchaos; hyperchaotic systems; circuit design; sliding mode control; memristors; hidden attractors; voice encryption.
  DOI: 10.1504/IJAAC.2019.10022589
   
  

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.
  
  

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: 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.