Event-Triggered Decentralized Tracking Control of Modular Reconfigurable Robots Through Adaptive Dynamic Programming

Abstract

This paper develops an event-triggered decentralized tracking control (DTC) approach for modular reconfigurable robots (MRRs) by using adaptive dynamic programming. By establishing a decentralized neural network (NN) observer, which uses local input–output data and desired states of coupling subsystems, the local dynamics of MRR subsystem can be obtained. In order to obtain the DTC, the tracking error subsystem is augmented by the exosystem with the desired trajectory. Based on the event-triggered mechanism and a modified local cost function, the DTC is derived by solving the local Hamilton–Jacobi–Bellman equation via a local critic NN with asymptotically stable structure. The stability of the entire closed-loop MRR system is analyzed by Lyapunov's direct method. The simulation of a two-degree of freedom MRR system ensures that the developed event-triggered DTC scheme is effective.