Adaptive dynamic event-triggered control for constrained modular reconfigurable robot

Abstract

Compared with traditional robot, modular reconfigurable robot (MRR) has the advantages of strong environmental adaptability and flexible task completion. According to the optimal tracking control problem (OTCP) of MRR under some restricted conditions, this paper puts forward a constrained dynamic event-triggered control (DETC) for MRR system with disturbance through adaptive dynamic programming (ADP), which can minimize the information interaction quantity under the premise of system stability and expected control effect. In view of the uncertainty of model coupling part, the identification network is used to estimate the dynamics of MRR and the estimation error is proved to be uniformly ultimate bounded (UUB). The other three groups of critic, action and disturbance neural networks (NNs) are established by the approximation principle of ADP. The optimal control pair is obtained through policy iteration (PI) with DETC, and the triggering condition is designed based on the asymptotic stability of MRR system. At last, the strengths of the algorithm in this paper are validated through simulation experiments.