Integral Sliding Mode Control for Partially Unknown T-S Fuzzy Systems Based on Reinforcement Learning Method

This article focus on the integral sliding mode control for a class of partially unknown T-S fuzzy systems on the basis of reinforcement learning technique. In the equivalent control, the control term is described as a form of feedback control based on the percompensation technique, such that the optimal control performance can be satisfied. Then the control gain is design by solving the algebraic Riccati equation according to the integral reinforcement learning (RL) method, and the requirements of ex-act information of system matrices is relaxed during the solving process. Base on the Lyapunov stability theory, the convergence and stability of the designed sliding mode control optimization scheme are analyzed. Finally, a numerical example is given to verified the effectiveness of the proposed theorems.