Event-triggered adaptive critic learning for the unmatched uncertain and asymmetric input-constrained systems using particle swarm optimization - Chu, Zhousheng; Liu, Chong; Yue, Hongyun; Li, Weihua (Nonlinear dynamics, 2025)
Practical applications often involveunmatched uncertainties and asymmetric input constraints. This paper focuses on designing a control strategy for a class of nonlinear systems with unmatched uncertainties and asymmetric input-constraints using event-triggered adaptive critic learning and particle swarm optimization algorithm (PSOA). The unmatched uncertainties are first decomposed to construct an auxiliary control system. Through theoretical analysis, the control policy is derived from a transformed optimal control problem by defining a performance function. A neural network (NN)-based adaptive critic learning technology, known as adaptive dynamic programming (ADP) method, is used to iteratively search for the optimal controller. Unlike the gradient descent algorithm, the PSOA is used to train the NN weights, which enhances the reliability of online learning. It is further proved that all signals in the closed-loop system are uniformly ultimately bounded (UUB). Additionally, to conserve communication and computational resources, an event-triggered mechanism is incorporated to update the controller. Finally, three detailed simulation results show the effectiveness of the proposed method.