Fuzzy Control For Nonlinear Systems With Uncertainty

With the progress of scientific and technology and the development of the productivity, control system, which is more and more complex, often is to lack a precise mathematical model, and is to have a high degree of nonlinearity and uncertainty, it is a important indicators for stability of the control system to evaluate the control system. However, because of the complexity of the fuzzy algorithm and structure, the uncertainty of language variable. therefore,the research to the stability of the fuzzy controller is to be very much difficult.In this paper, the control system based on T-S model, widely used in the current, combines with rubust control, parallel distributed compensation algorithm (PDC) and LMI technique, the stability question of T-S fuzzy systems is in-depth researched, the control question of the nonlinearity and uncertainty is studyed in T-S fuzzy system, the study of the design of the fuzzy controler and stability conditions of the relaxation. Main work are as follow:In the first chapter the background of this paper, the development of fuzy control and the state of study in the nonlinear systems with uncertainty are presented. The main work of the paper are stated.In the second chapter, the corresponding preliminary knowledge and fundamental theorem is given.In the third chapter, the stability conditions the continuous fuzzy system and the discrete fuzzy system and the corresponding the design questions of the controler are studyed, the study of the stability conditions of the relaxation. At last, the effectiveness of the stability conditions of the relaxation is demonstrated via numerical examples.In the forth chapter, the stability conditions of T-S fuzzy system and the design of fuzzy controller are researched. Take into account the stability conditions of the T-S fuzzy system, it is very conservative, new stability conditions is proposed for the T-S fuzzy system, applicate MATLAB, based on linear matrix inequlities LMI toolbox, new stability conditions is demonstrated to be reasonable and effective by a simulation example. Finally, there is a summary for the uncertain and nonlinear systems and some expectations of the fuzzy control for nonlinear systems with uncertainty are given.