Neural network-based event-triggered integral reinforcement learning for constrained [formula omitted] tracking control with experience replay

Abstract

Since input constraints and external disturbances are unavoidable in tracking control problems, how to obtain a controller in this case to save communication and data resources at the same time is very challenging. Aiming at these challenges, this paper develops a novel neural network (NN)-based event-triggered integral reinforcement learning (IRL) algorithm for constrained H∞ tracking control problems. First, the constrained H∞ tracking control problem is transformed into a regulation problem. Second, an event-triggered optimal controller is designed to reduce network transmission burden and improve resource utilization, where a novel threshold is proposed and its non-negativity can be guaranteed. Third, for implementation purpose, a novel NN-based event-triggered IRL algorithm is developed. In order to improve data utilization, the experience replay technique with an easy-to-verify condition is employed in the learning process. Theoretical analysis proves that the tracking error and weight estimation error are uniformly ultimately bounded. Finally, simulation verification shows the effectiveness of the present method.