Robust self-tuning fuzzy tracker design of time-varying nonlinear systems

This paper presents a search strategy to identify nonlinear dynamic systems as time-varying fuzzy model by modeling performance index. We introduce the fuzzy Lyapunov function to design the robust fuzzy tracker of the unknown nonlinear system with an H
∞
performance index based on the modeling error. In addition, we propose a compound search strategy of robust gains called conditional linear matrix inequality (CLMI) approach which was composed of the proposed improved random optimal algorithms (IROA). Moreover, the self-tuning gains are optimized by the cost function of IROA. Finally, a chaotic example is given to illustrate the utility of the proposed design method.