Intelligent fuzzy control with imperfect premise matching concept for complex nonlinear multiplicative noised systems
This paper provides the criterion for discussing stability analysis and synthesis of a class of complex nonlinear systems represented by TakagiSugeno fuzzy models with multiplicative noise. In the consequent part of the TS fuzzy models, the Its stochastic differential equations are introduced to represent the linear subsystems with multiplicative noise. Under the concept of imperfect premise matching, a novel fuzzy controller is designed without limitation of sharing the same membership function of fuzzy models. In other words, the imperfect premise matching technique provides a more general approach in designing fuzzy controllers. The advantage of the proposed fuzzy controller design method is that it can be enhanced more flexibility and robustness than well-known parallel distributed compensation based fuzzy control approach. At last, two numerical examples are given to illustrate the usefulness and effectiveness of proposed fuzzy controller design method.
imperfect premise matching takagisugeno fuzzy model nonlinear system with multiplicative noise linear matrix inequality