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In this paper,a novel design method for determining the optimal proportional-integral-derivative (PID) controller parameters for Takagi-Sugeno fuzzy model using the particle swarm optimization (PSO) algorithm is presented. In order to assist estimating the performance of the proposed PSO-PID controller,a new timedomain performance criterion function has been used. The proposed approach yields better solution in term of rise time, settling time,maximum overshoot and steady state error condition of the system.the proposed method was indeed more efficient and robust in improving the step response.

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Purpose – The purpose of this paper is to propose a new type of predictive fuzzy controller. The desired nonlinear system behavior is described by a set of Takagi-Sugeno (T-S) model. However, due to the complexity of the real processes, obtaining a high quality control with a short settle time, a periodical step response and zero steady-state error is often a difficult task. Indeed, conventional model predictive control (MPC) attempts to minimize a quadratic cost over an extended control horizon. Then, the MPC is insufficient to adapt to changes in system dynamics which have characteristics of complex constraints. In addition, it is shown that the clustering algorithm is sensitive to random initialization and may affect the quality of obtaining predictive fuzzy controller. In order to overcome these problems, chaos particle swarm optimization (CPSO) is used to perform model predictive controller for nonlinear process with constraints. The practicality and effectiveness of the identification and control scheme is demonstrated by simulation results involving simulations of a continuous stirred-tank reactor. Design/methodology/approach – A new type of predictive fuzzy controller. The proposed algorithm based on CPSO is used to perform model predictive controller for nonlinear process with constraints. Findings – The results obtained using this the approach were comparable with other modeling approaches reported in the literature. The proposed control scheme has been show favorable results either in the absence or in the presence of disturbance compared with the other techniques. It confirms the usefulness and robustness of the proposed controller. Originality/value – This paper presents an intelligent model predictive controller MPC based on CPSO (MPC-CPSO) for T-S fuzzy modeling with constraints.

This paper describes the development of a method to optimally tune constrained MPC algorithms for a nonlinear process. The T-S model is firstly established for nonlinear systems and its sequence parameters of fuzzy rules are identified by local recursive least square method. The proposed method is obtained by minimizing performance criteria in the worst-case conditions to control the process system, thus assuring robustness to the set of optimum tuning parameters. The resulting constrained mixed-integer nonlinear optimization problem is solved on the basis of a version of the particle swarm optimization technique. The practicality and effectiveness of the identification and control scheme is demonstrated by simulation results.