This study deals with the problem of controlling a class of uncertain non-linear systems in the p... more This study deals with the problem of controlling a class of uncertain non-linear systems in the presence of external disturbances. To achieve this goal, a novel optimal Type-2 fuzzy proportional integral derivative (OT2FPID) controller is introduced. In the proposed controller, a novel heuristic algorithm namely particle swarm optimisation with random inertia weight (RNW-PSO) is employed. To achieve an optimal performance, the parameters of the proposed controller as well as the input and output membership functions are optimised simultaneously by RNW-PSO. To evaluate the performance of the proposed controller, the results are compared with those obtained by optimal H ∞ adaptive proportional integral derivative controller, which is the latest research in the problem in hand. Simulation results show the effectiveness of the OT2FPID controller.
This paper introduces an optimal H 1 adaptive PID (OHAPID) control scheme for a class of nonlinea... more This paper introduces an optimal H 1 adaptive PID (OHAPID) control scheme for a class of nonlinear chaotic system in the presence system uncertainties and external disturbances. Based on Lyapunov stability theory, it is shown that the proposed control scheme can guarantee the stability robustness of closed-loop system with H 1 tracking performance. In the core of proposed controller, to achieve an optimal performance of OHAPID, the Particle Swarm Optimization (PSO) algorithm is utilized. To show the feasibility of proposed OHA-PID controller, it is applied on the chaotic gyro system. Simulation results demonstrate that it has highly effective in providing an optimal performance.
An important problem in engineering is the unknown parameters estimation in nonlinear systems. In... more An important problem in engineering is the unknown parameters estimation in nonlinear systems. In this paper, a novel adaptive particle swarm optimization (APSO) method is proposed to solve this problem. This work considers two new aspects, namely an adaptive mutation mechanism and a dynamic inertia weight into the conventional particle swarm optimization (PSO) method. These mechanisms are employed to enhance global search ability and to increase accuracy. First, three well-known benchmark functions namely Griewank, Rosenbrock and Rastrigrin are utilized to test the ability of a search algorithm for identifying the global optimum. The performance of the proposed APSO is compared with advanced algorithms such as a nonlinearly decreasing weight PSO (NDWPSO) and a real-coded genetic algorithm (GA), in terms of parameter accuracy and convergence speed. It is confirmed that the proposed APSO is more successful than other aforementioned algorithms. Finally, the feasibility of this algorithm is demonstrated through estimating the parameters of two kinds of highly nonlinear systems as the case studies.
The non-convex behavior presented by nonlinear systems limits the application of classical optimi... more The non-convex behavior presented by nonlinear systems limits the application of classical optimization techniques to solve optimal control problems for these kinds of systems. This paper proposes a hybrid algorithm, namely BA-SD, by combining Bee algorithm (BA) with steepest descent (SD) method for numerically solving nonlinear optimal control (NOC) problems. The proposed algorithm includes the merits of BA and SD simultaneously. The motivation of presenting the proposed algorithm includes that BA is showed to converge to the region that global optimum is settled, rapidly during the initial stages of its search. However, around global optimum, the search process will become slowly. In contrast, SD method has low ability to convergence to local optimum, but it can achieve faster convergent speed around global optimum and the convergent accuracy can be higher. In the proposed algorithm, at the beginning step of search procedure, BA is utilized to find a near optimum solution. In this case, the hybrid algorithm is used to enhance global search ability. When the change in fitness value is smaller than a predefined value, the searching procedure is switched to SD to accelerate the search procedure and find an accurate solution. In this way, the algorithm finds an optimum solution more accurately. Simulations demonstrate the feasibility of the proposed algorithm.
Journal of Software Engineering and Applications, 2010
This paper presents a novel modified particle swarm optimization algorithm (MPSO) for both offlin... more This paper presents a novel modified particle swarm optimization algorithm (MPSO) for both offline and online parametric identification of dynamic models. The MPSO is applied for identifying a suspension system introduced by a quarter-car model. A novel mutation mechanism is employed in MPSO to enhance global search ability and increase convergence speed of basic PSO (BPSO) algorithm. MPSO optimization is used to find the optimum values of parameters by minimizing the sum of squares error. The performance of the MPSO is compared with other optimization methods including BPSO and Genetic Algorithm (GA) in offline parameter identification. The simulating results show that this algorithm not only has advantage of convergence property over BPSO and GA, but also can avoid the premature convergence problem effectively. The MPSO algorithm is also improved to detect and determine the variation of parameters. This novel algorithm is successfully applied for online parameter identification of suspension system.
Journal of Dynamic Systems, Measurement, and Control, 2008
This paper presents a simple structure design for bilateral teleoperation systems with uncertaint... more This paper presents a simple structure design for bilateral teleoperation systems with uncertainties in time delay in communication channel. The goal is to achieve complete transparency and robust stability for the closed-loop system. For transparency, two local controllers are designed for the bilateral teleoperation systems. One local controller is responsible for tracking the master commands, and the other one is in charge of force tracking as well as guaranteeing the stability of the closed-loop system in presence of uncertainties in time delay. The stability analysis will be shown analytically for two cases: I) the possibly stability and II) the intrinsically stability. Moreover, in case II, in order to generate the proper inputs for the master controller in presence of uncertainties in time delay, an adaptive FIR filter is designed to estimate the time delay. The advantages of the proposed method are three folds: 1) stability of the closed-loop system is guaranteed under some mild conditions, 2) the whole system is transparent, and 3) design of the local controllers is simple. Simulation results show good performance of the proposed method.
... method were compared with some heuristic optimization algorithms such as GA, PSO and DE on CS... more ... method were compared with some heuristic optimization algorithms such as GA, PSO and DE on CSTCR problem. The results depicted that the proposed method is more robust and accurate than other algorithms. Alireza Alfi, Alireza Khosravi, Seyyed Ehsan Razavi, GJ P&A Sc ...
This paper presents a novel structure design for bilateral teleoperation control systems with som... more This paper presents a novel structure design for bilateral teleoperation control systems with some perturbations in time delay in communication channel. Transparency is used as an index to evaluate the performance of the teleoperation system. The focus of this paper is to achieve transparency for bilateral teleoperation system in presence of variations in time delay in communication channels as well as stability. To achieve transparency in the proposed structure, two controllers are used for bilateral teleoperation. The controllers force the slave manipulator to follow the master in spite of small variable time delays in communication channel. An adaptive FIR filter estimates the time delay. Furthermore, the stability of the closed-loop system despite estimator error in adaptive filter will be proved. The advantages of the proposed method are simple design and flexibility of the control method. Simulation results show very good and promising results despite small and varying time delay. Moreover, the proposed method provides a technique for predicting the time delay in order to avoid system instability.
In recent years, use of artificial neural networks have increased for estimation of Hardgrove gri... more In recent years, use of artificial neural networks have increased for estimation of Hardgrove grindability index (HGI) of coals. For training of the neural networks, gradient descent methods such as Backpropagaition (BP) method are used frequently. However they originally showed good performance in some non-linearly separable problems, but have a very slow convergence and can get stuck in local minima. In this paper, to overcome the lack of gradient descent methods, a novel particle swarm optimization and artificial neural network was employed for predicting the HGI of Kentucky coals by featuring eight coal parameters. The proposed approach also compared with two kinds of artificial neural network (generalized regression neural network and back propagation neural network). Results indicate that the neural networks -particle swarm optimization method gave the most accurate HGI prediction.
This study deals with the problem of controlling a class of uncertain non-linear systems in the p... more This study deals with the problem of controlling a class of uncertain non-linear systems in the presence of external disturbances. To achieve this goal, a novel optimal Type-2 fuzzy proportional integral derivative (OT2FPID) controller is introduced. In the proposed controller, a novel heuristic algorithm namely particle swarm optimisation with random inertia weight (RNW-PSO) is employed. To achieve an optimal performance, the parameters of the proposed controller as well as the input and output membership functions are optimised simultaneously by RNW-PSO. To evaluate the performance of the proposed controller, the results are compared with those obtained by optimal H ∞ adaptive proportional integral derivative controller, which is the latest research in the problem in hand. Simulation results show the effectiveness of the OT2FPID controller.
This paper introduces an optimal H 1 adaptive PID (OHAPID) control scheme for a class of nonlinea... more This paper introduces an optimal H 1 adaptive PID (OHAPID) control scheme for a class of nonlinear chaotic system in the presence system uncertainties and external disturbances. Based on Lyapunov stability theory, it is shown that the proposed control scheme can guarantee the stability robustness of closed-loop system with H 1 tracking performance. In the core of proposed controller, to achieve an optimal performance of OHAPID, the Particle Swarm Optimization (PSO) algorithm is utilized. To show the feasibility of proposed OHA-PID controller, it is applied on the chaotic gyro system. Simulation results demonstrate that it has highly effective in providing an optimal performance.
An important problem in engineering is the unknown parameters estimation in nonlinear systems. In... more An important problem in engineering is the unknown parameters estimation in nonlinear systems. In this paper, a novel adaptive particle swarm optimization (APSO) method is proposed to solve this problem. This work considers two new aspects, namely an adaptive mutation mechanism and a dynamic inertia weight into the conventional particle swarm optimization (PSO) method. These mechanisms are employed to enhance global search ability and to increase accuracy. First, three well-known benchmark functions namely Griewank, Rosenbrock and Rastrigrin are utilized to test the ability of a search algorithm for identifying the global optimum. The performance of the proposed APSO is compared with advanced algorithms such as a nonlinearly decreasing weight PSO (NDWPSO) and a real-coded genetic algorithm (GA), in terms of parameter accuracy and convergence speed. It is confirmed that the proposed APSO is more successful than other aforementioned algorithms. Finally, the feasibility of this algorithm is demonstrated through estimating the parameters of two kinds of highly nonlinear systems as the case studies.
The non-convex behavior presented by nonlinear systems limits the application of classical optimi... more The non-convex behavior presented by nonlinear systems limits the application of classical optimization techniques to solve optimal control problems for these kinds of systems. This paper proposes a hybrid algorithm, namely BA-SD, by combining Bee algorithm (BA) with steepest descent (SD) method for numerically solving nonlinear optimal control (NOC) problems. The proposed algorithm includes the merits of BA and SD simultaneously. The motivation of presenting the proposed algorithm includes that BA is showed to converge to the region that global optimum is settled, rapidly during the initial stages of its search. However, around global optimum, the search process will become slowly. In contrast, SD method has low ability to convergence to local optimum, but it can achieve faster convergent speed around global optimum and the convergent accuracy can be higher. In the proposed algorithm, at the beginning step of search procedure, BA is utilized to find a near optimum solution. In this case, the hybrid algorithm is used to enhance global search ability. When the change in fitness value is smaller than a predefined value, the searching procedure is switched to SD to accelerate the search procedure and find an accurate solution. In this way, the algorithm finds an optimum solution more accurately. Simulations demonstrate the feasibility of the proposed algorithm.
Journal of Software Engineering and Applications, 2010
This paper presents a novel modified particle swarm optimization algorithm (MPSO) for both offlin... more This paper presents a novel modified particle swarm optimization algorithm (MPSO) for both offline and online parametric identification of dynamic models. The MPSO is applied for identifying a suspension system introduced by a quarter-car model. A novel mutation mechanism is employed in MPSO to enhance global search ability and increase convergence speed of basic PSO (BPSO) algorithm. MPSO optimization is used to find the optimum values of parameters by minimizing the sum of squares error. The performance of the MPSO is compared with other optimization methods including BPSO and Genetic Algorithm (GA) in offline parameter identification. The simulating results show that this algorithm not only has advantage of convergence property over BPSO and GA, but also can avoid the premature convergence problem effectively. The MPSO algorithm is also improved to detect and determine the variation of parameters. This novel algorithm is successfully applied for online parameter identification of suspension system.
Journal of Dynamic Systems, Measurement, and Control, 2008
This paper presents a simple structure design for bilateral teleoperation systems with uncertaint... more This paper presents a simple structure design for bilateral teleoperation systems with uncertainties in time delay in communication channel. The goal is to achieve complete transparency and robust stability for the closed-loop system. For transparency, two local controllers are designed for the bilateral teleoperation systems. One local controller is responsible for tracking the master commands, and the other one is in charge of force tracking as well as guaranteeing the stability of the closed-loop system in presence of uncertainties in time delay. The stability analysis will be shown analytically for two cases: I) the possibly stability and II) the intrinsically stability. Moreover, in case II, in order to generate the proper inputs for the master controller in presence of uncertainties in time delay, an adaptive FIR filter is designed to estimate the time delay. The advantages of the proposed method are three folds: 1) stability of the closed-loop system is guaranteed under some mild conditions, 2) the whole system is transparent, and 3) design of the local controllers is simple. Simulation results show good performance of the proposed method.
... method were compared with some heuristic optimization algorithms such as GA, PSO and DE on CS... more ... method were compared with some heuristic optimization algorithms such as GA, PSO and DE on CSTCR problem. The results depicted that the proposed method is more robust and accurate than other algorithms. Alireza Alfi, Alireza Khosravi, Seyyed Ehsan Razavi, GJ P&A Sc ...
This paper presents a novel structure design for bilateral teleoperation control systems with som... more This paper presents a novel structure design for bilateral teleoperation control systems with some perturbations in time delay in communication channel. Transparency is used as an index to evaluate the performance of the teleoperation system. The focus of this paper is to achieve transparency for bilateral teleoperation system in presence of variations in time delay in communication channels as well as stability. To achieve transparency in the proposed structure, two controllers are used for bilateral teleoperation. The controllers force the slave manipulator to follow the master in spite of small variable time delays in communication channel. An adaptive FIR filter estimates the time delay. Furthermore, the stability of the closed-loop system despite estimator error in adaptive filter will be proved. The advantages of the proposed method are simple design and flexibility of the control method. Simulation results show very good and promising results despite small and varying time delay. Moreover, the proposed method provides a technique for predicting the time delay in order to avoid system instability.
In recent years, use of artificial neural networks have increased for estimation of Hardgrove gri... more In recent years, use of artificial neural networks have increased for estimation of Hardgrove grindability index (HGI) of coals. For training of the neural networks, gradient descent methods such as Backpropagaition (BP) method are used frequently. However they originally showed good performance in some non-linearly separable problems, but have a very slow convergence and can get stuck in local minima. In this paper, to overcome the lack of gradient descent methods, a novel particle swarm optimization and artificial neural network was employed for predicting the HGI of Kentucky coals by featuring eight coal parameters. The proposed approach also compared with two kinds of artificial neural network (generalized regression neural network and back propagation neural network). Results indicate that the neural networks -particle swarm optimization method gave the most accurate HGI prediction.
This paper presents application of a new Meta-heuristic algorithm, namely Search Group Algorithm ... more This paper presents application of a new Meta-heuristic algorithm, namely Search Group Algorithm (SGA), compared with Genetic Algorithm (GA) and Particle Swarm Optimization (PSO) for tuning of an optimal PID type fuzzy controller by minimizing the Integral of Time Multiplied Absolute Error (ITAE) and squared control signal in a Networked Control System (NCS). The paper compares the closed loop performances of a higher order and a time delay system and shows that PID type fuzzy controller tuned by SGA have better performance in controlling random time delay and a unit change in set point over optimal conventional PID controller.
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Papers by Alireza Alfi