Integral Reinforcement Learning-Based Adaptive NN Control for Continuous-Time Nonlinear MIMO Systems with Unknown Control Directions

In this paper, an integral reinforcement learning-based adaptive neural network (NN) tracking control is developed for the continuous-time (CT) nonlinear system with unknown control directions. The long-term performance index in the CT domain is prescribed. Critic and action NNs are designed to approximate the unavailable long-term performance index and the unknown dynamics, respectively. The reinforcement signal is explicitly embedded in the updated law of the action NN and then the estimated long-term performance index can be minimized. Rigorous theoretical analysis is provided to show that the closed-loop system is stabilized and all closed-loop signals are semiglobally uniformly ultimately bounded. Finally, to demonstrate the control performance, simulation results are provided to verify the tacking control performance of an autonomous underwater vehicle model.