Event-triggered constrained control using explainable global dual heuristic programming for nonlinear discrete-time systems

This paper develops an event-triggered optimal control method that can deal with asymmetric input constraints for nonlinear discrete-time systems. The implementation is based on explainable global dual heuristic programming (XGDHP) technique. Different from traditional GDHP, the required derivatives of cost function in proposed are computed by explicit analytical calculations, which makes XGDHP more explainable. Besides, challenge caused overcome combination a piece-wise utility and bounding layer actor network. Furthermore, mechanism introduced to decrease amount computation, stability analysis provided fewer assumptions compared most existing studies investigate using adaptive dynamic programming. Two simulation carried out demonstrate applicability constructed approach. results present developed algorithm substantially save computational load, while maintain comparable performance time-based