Fuzzy Predictive Control Based On T-S Model

With the cons ant development of control theory, the plant studied in practical system is more and more complicated. On the one hand, the complexity exhibits that the control system have many characteristics, such as high coupling, temporal variability, undetermined and deep nonlinear property. Predictive control is developed based on solving this problem. The early predictive control algorithm applied in industry includes Impulse Model Predictive Heuristic Control(MPHC) based on parametric model impulse response which proposed by Richalet and Mehra, Dynamic Matrix Control(DMC) based on nonparametric step response which proposed by Cultes, Clarke's Generalized Predictive Control(GPC) and Lelic's Generalized Predictive Pole deploying control, which attained good effect in process control of complicated produce. And on the other hand, the complexity extremely displays that the information got from actual plant decreased relatively, which made model and control of the plant become more difficult. However, the classical control theory is difficult to get good control effect and even can not control the plant. Fuzzy identification hold immense potential in solving control problem of complicated system, its abilities of accepting language information, its structure including membership function, fuzzy rule, defuzzify and its nonlinear property and collateral disposal make itself can solve much multivariable control problem of nonlinear system in industrial fields. This paper concentrates the researches foreland of the control science and engineering subject, namely the control of complicated system, and research further. The main idea is that fuzzy identification and predictive control combined to use respective merits, which proved effective.That discretional close in upon nonlinear system is the theoretic gist of fuzzy logic system to identify process of complicated industry and educe reasonable control. Primary production is gained in this research. Mamdani and T-S fuzzy model were proved the universal tool to cose in upon. According to the characteristics that difficult to model and control for the process of complicated industry, that T-S fuzzy dynamic model who combined data information with language information effectively applied to identify and control was proposed by the paper, which started with the structure of T-S fuzzy model, elicited the steps and algorithm of the identification for T-S fuzzy model, then educed idiographic algorithm of fuzzy controller from systemizing to design T-S fuzzy model. Finally, analyzed the stability of model in succession. The algorithm is proved effective by applying to simulation experiment for inw rted pendulum control.Based on intrcducing the basal principle of predictive control and T-S fuzzy model, the paper divided the predictive control based on T-S fuzzy model into two parts, one is indirect fuzzy predictive control, the other is direct fuzzy predictive control designed by combining T-S fuzzy model with predictive controller, namely generalized predictive control(based on T-S fuzzy model, And it educed respective algorithm steps by detailed theory reasoning and analysis. The algor thm proved to be effective and feasible according to the partial simulation experiment for non inear system.The paper researched from T-S fuzzy model identification, combination of fuzzy identification and predictive control. And it gained some productions, which lay an important academic foundation for in-depth research further in such fields.