Mohammad Reza Soltanpour

An optimal and intelligent control strategy for a class of nonlinear systems: adaptive fuzzy sliding mode

In this paper, an optimal adaptive fuzzy sliding mode controller is presented for a class of nonlinear systems. In the proposed control, in the beginning, the boundaries of parametric uncertainties, disturbances and un-modeled dynamics are reduced using a feedback linearization approach. Next, in order to overcome the remaining uncertainties, a sliding mode controller is designed. Mathematical proof shows that the closed-loop system with the proposed control is globally asymptotically stable. Using sliding mode control causes the undesirable chattering phenomenon to occur in the control input. Next, in order to remove the undesirable chattering phenomenon, an adaptive fuzzy approximator is designed to approximate the maximum boundary of the remaining uncertainties. Another mathematical proof shows that the closed-loop system with the proposed control is globally asymptotically stable in the presence of structured and unstructured uncertainties, and external disturbances. Finally, the self-adaptive modified bat algorithm is used to determine the coefficients of the adaptive fuzzy sliding mode control and the coefficients of the membership functions of the adaptive fuzzy approximator. To investigate the performance of the proposed controller, an inverted pendulum system is used as a case study. Simulation results verify the desirable performance of the optimal adaptive fuzzy sliding mode control.

Robust fuzzy sliding mode control for tracking the robot manipulator in joint space and in presence of uncertainties

This paper proposes a simple fuzzy sliding mode control to achieve the best trajectory tracking for the robot manipulator. In the core of the proposed method, by applying the feedback linearization technique, the known dynamics of the robots manipulator is removed; then, in order to overcome the remaining uncertainties, a classic sliding mode control is designed. Afterward, by applying the TS fuzzy model, the classic sliding mode controller is converted to fuzzy sliding mode controller with very simple rule base. The mathematical analysis shows that the robot manipulator with the new proposed control in tracking the robot manipulator in presence of uncertainties has the globally asymptotic stability. Finally, to show the performance of the proposed method, the controller is simulated on a robot manipulator with two degrees of freedom as case study of the research. Simulation results demonstrate the superiority of the proposed control scheme in presence of the structured and unstructured uncertainties.

Swarm optimization tuned fuzzy sliding mode control design for a class of nonlinear systems in presence of uncertainties

This paper provides an optimal controlling approach for a class of nonlinear systems with structured and unstructured uncertainties using fuzzy sliding mode control. First known dynamics of the system are eliminated through feedback linearization and then optimal fuzzy sliding mode controller is designed using an intelligent fuzzy controller based on Sugeno-Type structure. The proposed controller is optimized by a novel heuristic algorithm namely Particle Swarm Optimization with random inertia Weight RNW-PSO. In order to handle, the uncertainties Lyapunov method is used. There are no signs of the undesired chattering phenomenon in the proposed method. The globally asymptotic stability of the closed-loop system is mathematically proved. Finally, this control method is applied to the inverted pendulum system as a case study. Simulation results show desirability of the system performance.

A novel self-adaptive modified bat fuzzy sliding mode control of robot manipulator in presence of uncertainties in task space

In this paper, an optimal fuzzy sliding mode controller has been designed for controlling the end-effector position in the task space. In the proposed control, feedback linearization method, sliding mode control, first-order fuzzy TSK system and optimization algorithm are utilized. In the proposed controller, a novel heuristic algorithm namely self-adaptive modified bat algorithm (SAMBA) is employed. To achieve an optimal performance, the parameters of the proposed controller as well as the input membership functions are optimized by SAMBA simultaneously. In this method, the bounds of structural and non-structural uncertainties are reduced by using feedback linearization method, and to overcome the remaining uncertainties, sliding mode control is employed. Mathematical proof demonstrates that the closed loop system with the proposed control has global asymptotic stability. The presence of sliding mode control gives rise to the adverse phenomenon of chattering in the end-effector position tracking in the task space. Subsequently, to prevent the occurrence of chattering in control input, a first-order TSK fuzzy approximator is utilized. Finally, to determine the fuzzy sliding mode controller coefficients, the optimization algorithm of Self-Adaptive Modified Bat is employed. To investigate the performance of the proposed control, a two-degree-of-freedom manipulator is used as a case study. The simulation results indicate the favorable performance of the proposed method.

A robust and new simple control strategy for a class of nonlinear power systems: induction and servomotors:

In this paper, a robust backstepping controller is presented for position tracking of a class of servomotors. It has been shown by mathematical proof that the closed-loop system with the proposed controller has global asymptotic stability in the presence of structured and unstructured uncertainties, and external disturbances. In the following sections of this paper, to remove the undesirable phenomenon of chattering in the proposed control input, using a Takagi-Sugeno-Kang fuzzy system a robust fuzzy backstepping controller is designed that does not suffer from the undesirable phenomenon of chattering. To investigate the performance of the proposed controller, an induction motor and a DC motor with uncertainties are used as case studies. In the simulation phase, to provide essential challenges for the proposed controllers, simulations have been implemented in two steps. The results of these simulations show that the proposed approaches are very robust in the presence of parametric uncertainties, especially in the presence of external disturbances. In the design of a robust fuzzy controller, practical implementation considerations are taken into account in a way where the control input has a low computational burden.

Voltage-Base Control of Robot Manipulator Using Adaptive Fuzzy Sliding Mode Control

In this paper, a controller is proposed that is able to overcome existing structured and unstructured uncertainties in the dynamic equations of robot manipulator and its actuators. In this method, at first, through sliding mode control and by using defined dynamic equations of robot manipulator, robust nonlinear controller is designed that is capable of overcoming the existing uncertainties. In the following, due to incidence of the control input chattering, a first-order TSK fuzzy approximator is designed in such a way that is able to overcome undesirable chattering phenomenon. The presented fuzzy sliding mode control has a small number of calculations. However, the design structure of proposed control is in such a way that leads to increase the number of needed sensors for the practical implementation of this controller. Next, to overcome these problems, an adaptive fuzzy approximator is used to approximate the bounds of the existing uncertainties. The proposed adaptive fuzzy sliding mode control has low volume of calculations, and due to the use of single-input, single-output fuzzy rules in the adaptive fuzzy approximator, the problem of the increasing number of sensors is resolved. Mathematical proof investigates that a closed-loop system with the proposed control and in the presence of existing uncertainties in the dynamic equations of robot manipulator and its actuators has global asymptotic stability. Finally, to demonstrate the performance of the proposed controller, a two-link elbow robot manipulator is used as a case study. The simulation results show the favorable efficiency of the proposed controller.