A learning-based approach to event-triggered guaranteed cost control for completely unknown nonlinear systems

This paper develops a novel guaranteed cost control (GCC) approach under the event-triggered mechanism for completely unknown systems using integral reinforcement learning (IRL) algorithm. First, based on the adaptive dynamic programming (ADP) method, the GCC problem is addressed by transforming it into the optimal control problem. Second, without using the accurate information of system dynamics, a model-free data-based GCC approach is designed via IRL algorithm. Moreover, for the purpose of reducing the waste of communication resources, a GCC algorithm is presented under the event-triggered mechanism for completely unknown system by utilizing the explorized IRL algorithm. The critic-actor-disturbance neural networks (NNs) are applied to approximate near optimal solution. In addition, the weight estimations of NNs are tuned synchronously according to the designed novel triggering condition. Furthermore, the stability analysis of the controlled system is given by utilizing the Lyapunov principle. Finally, the simulation results are presented to verify the feasibility of the designed approach.