Decentralized Self-triggered Approximate Optimal Tracking Control of Modular Reconfigurable Robots Via Adaptive Dynamic Programming

This paper presents a decentralized self-triggered tracking control method for modular reconfigurable robots (MRRs) via adaptive dynamic programming (ADP). The Newton-Euler form dynamic modeling method is used to model the n- DOF MRR system. Based on self-triggering mechanism and local cost function, utilizing local critic neural network (NN) solve local Hamilton-Jacobi-Bellman (HJB) equation to obtain optimal tracking control. The self-triggered mechanism was introduced, which uses the system state at the triggering instant to predict the next triggering instant and update the control strategy, thereby reducing resource waste while avoiding the continuous moni-toring of system state under event-triggered conditions. Using Lyapunov theory for stability analysis ensures that the tracking error of the closed-loop MRR system is ultimately bounded (UUB). Finally, using 2-DOF MRR experimental platform to verify the proposed control is feasible.