Predictor-Based Self-Organizing Control for Unknown Nonlinear Dynamical Systems

The complex dynamics and strong nonlinearity characteristics will bring obstacle for the accurate and stable tracking control performance of unknown nonlinear systems. To solve this problem, a predictor-based self-organizing control (PSOC) method is designed to improve the control performance in this article. The merits of PSOC are three aspects. First, a self-organizing fuzzy neural network (SOFNN) is designed to adaptively approximate the strong nonlinearity of the system. Then, the self-organizing strategy, based on rule similarity and validity, is studied to adjust the number of fuzzy rules in SOFNN to improve the approximate accuracy. Second, a dynamic surface control scheme is designed to describe the system dynamics. Then, the dynamic surface control law is obtained to achieve the stable control. Third, a state predictor is developed to predict the state variable accurately. Then, the prediction error is utilized to design the adaptive law of PSOC to reduce the variation range of tracking control errors and further improve the control accuracy. Finally, the numerical simulations and a detailed comparison study are given to evaluate the efficiency of the proposed PSOC method.