Fuzzy Modeling, Adaptive Control And Its Applications For Nonlinear Systems

The complex industry processes in a varying operation conditions are often nonlinear and mulitivariable with uncertainties and strong coupling. So the exact mathematical model cannot be determined with ease. The foundation of fuzzy modeling and adaptive control of nonlinear systems is of artificial intelligence, which has the abilities such as self-studying and adaptive ability, automatic information processing ability to abate uncertainties and the programming ability to reliably complete control and can achieve effective control of the complex systems. So the research on fuzzy modeling and adaptive control of nonlinear system is of significant academic and practical values to control systems of complex uncertainties.The fuzzy modeling and adaptive control are one of effective methods of nonlinear system control. In recent years, the fuzzy modeling and adaptive control of nonlinear systems have obtained significant development. From the view of practice, the fuzzy modeling and adaptive control have shown great potential in solving the control problem of complex systems. But the problems need to be coped with that how to build the effective fuzzy rules database from the sampling data, how to construct the fuzzy approacher, how to develop fuzzy controller to achieve better performance and how to handle the problem of the state variables cannot be measured.In this paper, the research of nonlinear system fuzzy modeling and adaptive control is developed and the engineering applications of fuzzy theory are explored. The works are as follows:1. Fuzzy rule's selection is one of the main factors affecting the accuracy of fuzzy model. For the classic WM method of paper [115], fuzzy rules database is lack of good completeness and robustness. In this paper, a data-mining method is used to construct the fuzzy rules database. The main advantage of the fuzzy rules base on the proposed method is its completeness and robustness. The contrast simulation shows that it can elevate the fuzzy model accuracy.2. For the case that the parameter adjustment of adaptive fuzzy control uses only the...