基于积分强化学习的学习感知型动态事件触发最优控制OA

Event-triggered mechanisms,particularly dynamic ones,have garnered significant interest in the control community,with a key challenge being the balance between control performance and resource utilization.This bal-ance becomes even more crucial when integrating these mechanisms into learning systems,where learning efficiency plays a vital role.This paper presents a learning-based dynamic event-triggered framework that combines optimal control formulation,learning-aware design,and integral reinforcement learning,allowing the system to adapt the triggering process based on learning status and state changes.Using only partial knowledge of the dynamics,an op-timal control policy can be learned online via a critic neural network,with data transmission flexibly regulated by dynamic triggering rules.This enables the system to intelligently adopt"busy sampling"when the weight changes dramatically,and switch to"idle sampling"during smooth learning periods to save communication/computational resources,thereby achieving an effective balance between control performance,learning efficiency,and resource con-sumption.Theoretical analysis rigorously proves the asymptotic stability of closed-loop systems and the uniform ul-timate boundedness of weight errors.Finally,the proposed method is comparatively verified on a benchmark nonlin-ear system and a single-link robotic arm system,indicating that it can achieve comparable or even better learning and control effects with less communication cost.