Optimal Adaptive Control of Constrained-Input Continuous-Time Systems Based on Synchronous Policy Iteration Method

In this paper, an online constrained optimal adaptive control method based on the reinforcement learning for partially-unknown nonlinear continuous-time systems with affine equations is presented. A distinctive feature of this paper is the proposal of a method to solve an algebraic Riccati equation online designed for constrained-input systems, all without requiring prior knowledge of the system’s complete internal dynamics model. The algorithm used is the Synchronous Policy Iteration Method Without requiring an initial stabilization control signal. Unlike the existing methods, actor and critic networks update each other simultaneously and instead of minimizing the Bellman error, a direct adaptive law for updating the weights is presented. The stability and convergence of the proposed method are proved by the Lyapunov theorem and the simulation results show the correctness and effectiveness of the controller performance.