An advanced robust integral reinforcement learning scheme with the fuzzy inference system

In this paper, the model-free robust control problem is investigated for nonlinear systems with a relaxed condition of initial admissible control. An advanced integral reinforcement learning method is developed, which merges the adaptive network-based fuzzy inference system (ANFIS) and pre-training of the initial weights. To loose the condition for choosing the initial control law, pre-training of initial weights is established by utilizing the ANFIS to provide the information corresponding to the system model, which is applicable to the model-free issue. Based on the actor-critic structure, the approximate optimal control law is obtained by employing adaptive dynamic programming. Redesigning the obtained control law, the robust controller can be derived to stabilize the system with the uncertain term. Eventually, two examples are utilized to verify the effectiveness of the constructed algorithm.