Adaptive neural event-triggered near-optimal control for affined uncertain nonlinear discrete-time system

A novel event-triggered heuristic dynamic programming (HDP) algorithm is proposed for the near-optimal control of uncertain discrete-time nonlinear input-affine systems. Based on input-to-state stability (ISS) analysis, a new event-triggered mechanism (ETM) is designed. Under constant coefficients, a Lipschitz-like assumption that forms the basis of the event-triggering condition is considered to be conservative. To further reduce the conservativeness of the triggering condition and enlarge the average interevent time, an adaptive threshold parameter is utilized in the proposed ETM. In the HDP algorithm framework, model, critic, and action network are adopted to achieve state estimation, approximation to the optimal cost function, and solution to Hamilton-Jacobian-Bellman (HJB) equation. Under the proposed event-triggered HDP algorithm, the closed system is proved to possess semiglobal uniform ultimate boundedness (SGUUB). Finally, by conducting simulation, it shows that on the premise of satisfying control performance, the event-triggered strategy can realize reduction on the updating frequency of the controller.