Online identifier-actor-critic algorithm for optimal control of nonlinear systems

In this paper, a novel identifier-actor-critic optimal control scheme is developed for discrete-time affine nonlinear systems with uncertainties. In contrast to traditional adaptive dynamic programming methodology, which requires at least partial knowledge of the system dynamics, a neural-network identifier is employed to learn the unknown control coefficient matrix working together with actor-critic-based scheme to solve the optimal control online. The critic network learns the approximate value function at each step. The actor network attempts to improve the current policy based on the approximate value function. The weights of all neural networks are updated at each sampling instant. Lyapunov theory is utilized to prove the stability of closed-loop system. It shows that system states and neural network weights are uniformly ultimately bounded. Finally, simulations are provided to illustrate the effectiveness of the developed method. Copyright (C) 2016 John Wiley & Sons, Ltd.