Neuro-Optimal Control of Unknown Nonaffine Nonlinear Systems with Saturating Actuators

Abstract This paper develops an adaptive optimal control for the infinite-horizon cost of unknown nonaffine nonlinear continuous-time systems with control constraints. A recurrent neural network (NN) is constructed to identify the unknown system dynamics with stability proof. Then, two feedforward NNs are used as the actor and the critic to approximate the optimal control and the optimal value, respectively. By using this architecture, the action NN and the critic NN are tuned simultaneously, without the requirement of the knowledge of system dynamics. In addition, the weights of the action NN and the critic NN are guaranteed to be uniformly ultimately bounded based on Lyapunov's direct method. A simulation example is provided to verify the effectiveness of the developed theoretical results.