Reinforcement learning-based adaptive tracking control for mismatched non-affine nonlinear systems with coupled uncertainties

This paper studies a reinforcement learning-based adaptive non-affine tracking control method for a class of uncertain mismatched non-affine nonlinear systems. The considered system is not only affected by external mismatched disturbances and internal uncertainties, but also influenced by the non-affine control structures. Firstly, an auxiliary integral system is developed for the purpose of isolating the non-affine control input. Secondly, by designing the actor-critic networks to evaluate the system control performance and generate the reinforcement signal, the unknown internal uncertainties can be handled. Thirdly, based on the output of reinforcement learning network, several disturbance compensation laws are constructed to address the adverse impact of matched and mismatched disturbances. As a result, a novel intelligent adaptive non-affine controller is proposed by integrating actor-critic reinforcement learning framework, disturbance compensation and adaptive laws. It has been proved that closed-loop system are stable and the tracking errors are bounded. The numerical simulation results show the effectiveness and superiority of the proposed method.