Neural-network based adaptive sliding mode control for Takagi-Sugeno fuzzy systems

In the present study, the adaptive sliding mode control (ASMC) strategy is investigated for a class of complex nonlinear systems with matched and unknown nonlinearities and external disturbances. The nonlinearities and external disturbances are approached by a Gaussian radial basic neural network. A Takagi-Sugeno (T-S) fuzzy model based integral switching function is introduced to solve the ASMC problem, which eliminates the constrain that input gains required to share a common matrix in all fuzzy rules. Then, the switching control term is represented as a proportional integral (PI) control format to reduce the chattering phenomenon. Based on the Lyapunov theory, a set of existence conditions of the sliding mode controller are given such that the stability of the control systems can be guaranteed. Finally, a experimental simulation is utilized to verify the effectiveness of the proposed sliding mode control (SMC) strategy.