Nonlinear Approximate Optimal Control Based on Integral Reinforcement Learning

Abstract

This paper aims to find the optimal control solution of an affine nonlinear continuous-time system with unknown input dynamic. Based on Critic-Actor neural network, an online integral reinforcement learning algorithm has been proposed. The algorithm solves the Bellman equation online, while Critic neural network is used to approximate the value function and Actor neural network is used for policy improvement. The policy evaluation and policy improvement of integral reinforcement learning are performed alternately until the performance of control systems no longer improves. By using Lyapunov function theory, all the weights of Critic-Actor neural network and the states of the system are guaranteed to be locally uniformly ultimately bounded. The simulation results show the effectiveness of the developed method.