On the development of a fuzzy model for nonlinear systems

In this study, we propose a fuzzy algorithm for modeling nonlinear physical systems. Each of the nonlinear coefficients in the system dynamic equation was modeled by a set of fuzzy rules. An identification algorithm incorporating a recursive least-square method and an optimum search process was then used to optimize the parameters of the fuzzy rules. This ensures that all unknown fuzzy parameters can be predicted systematically. The feasibility of such an algorithm was demonstrated by two examples, a 2-link manipulator and a servovalve-controlled pneumatic chamber. Both computer simulation and experimental results have shown that the proposed fuzzy algorithm indeed is very useful for modeling nonlinear systems as the predicted system responses match the actual ones very well.