DSC Approach to Robust Adaptive Fuzzy Tracking Control for Strict-Feedback Nonlinear Systems

An adaptive tracking problem is discussed for a class of strict-feedback uncertain systems. Combined "dynamic surface control" technique with "minimal learning parameters" algorithm, a robust adaptive fuzzy tracking controller is developed by use of Takagi-Sugeno (T-S) fuzzy systems as approximators. The proposed algorithms can solve both problems of "dimension curse" and "explosion of complexity" synchronously, especially, the number of parameters updated on line for each subsystem is reduced to 2. Additionally, the possible controller singularity problem can also be removed. Thereby, the algorithm is convenient to implement in applications. The stability of closed-loop is proved. Finally, simulation results demonstrate the effectiveness and performance.