Research on Fuzzy I-PD Preview Control for Nonlinear System

In this paper, we propose a new approach on nonlinear preview control with fuzzy logic. Because preview control can decrease not only the overshoot but also the control energy, there have been many approaches on preview control. However, as these approaches are based on linearized models(18), they cannot get a good result when they are applied to nonlinear plants. So we propose a fuzzy preview control to compensate the nonlinear properties of plant. Here, first, a simple Fuzzy Logic Control (S-FLC) with single-input-single-output is introduced. This kind of Fuzzy Logic Control can decrease the number of rules and make the fuzzy reasoning be more quickly. Second, using the I-PD control system proposed by T. Kitamori, fuzzy I-PD control's scaling factors are designed with matching technique. Then to compensate nonlinear properties of plant, two parameters are injected and optimized with Genetic Algorithm (GA). Third, a new designing scheme of preview control element using fuzzy theory is proposed and the preview control element is optimized with GA under a performance criteria. Being designed on the nonlinear model, it will make the fuzzy preview control be more suitable to nonlinear plant. At last, some experiments with a two-cascaded tank system illustrate the efficiency of the proposed approach.

[1]  Shuta Murakami,et al.  Self-Tuning Fuzzy Controller , 1988 .

[2]  Myung Jin Chung,et al.  Systematic design and stability analysis of a fuzzy logic controller , 1995 .

[3]  K. Tang,et al.  Comparing fuzzy logic with classical controller designs , 1987, IEEE Transactions on Systems, Man, and Cybernetics.

[4]  C. L. Karr,et al.  Fuzzy control of pH using genetic algorithms , 1993, IEEE Trans. Fuzzy Syst..

[5]  M.A. Lee,et al.  Integrating design stage of fuzzy systems using genetic algorithms , 1993, [Proceedings 1993] Second IEEE International Conference on Fuzzy Systems.

[6]  Chuen-Chien Lee FUZZY LOGIC CONTROL SYSTEMS: FUZZY LOGIC CONTROLLER - PART I , 1990 .

[7]  Wang Dong,et al.  A Design Method of MI-MO I-PD Preview Control System with Partial Model Matching Techniques. , 2001 .

[8]  Takashi Shigemasa,et al.  A Practical Reference Model for Control System Design , 1983 .

[9]  Ebrahim H. Mamdani,et al.  A linguistic self-organizing process controller , 1979, Autom..

[10]  Tzuu-Hseng S. Li,et al.  Design of a GA-based fuzzy PID controller for non-minimum phase systems , 2000, Fuzzy Sets Syst..

[11]  Lawrence. Davis,et al.  Handbook Of Genetic Algorithms , 1990 .

[12]  Ching-Chang Wong,et al.  A self-generating method for fuzzy system design , 1999, Fuzzy Sets Syst..

[13]  Chang Chieh Hang,et al.  Tuning and analysis of a fuzzy PI controller based on gain and phase margins , 1998, IEEE Trans. Syst. Man Cybern. Part A.

[14]  Byung Kook Kim,et al.  Design of a single-input fuzzy logic controller and its properties , 1999, Fuzzy Sets Syst..

[15]  Toshiyuki Kitamori A Design Method for Sampled-Data Control Systems Based upon Partial Knowledge about Controlled Processes , 1979 .