Compensator-critic structure-based event-triggered decentralized tracking control of modular robot manipulators: theory and experimental verification

Abstract This paper presents a novel compensator-critic structure-based event-triggered decentralized tracking control of modular robot manipulators (MRMs). On the basis subsystem dynamics under joint torque feedback (JTF) technique, proposed error fusion function, which includes position and velocity error, is utilized to construct performance index function. By analyzing dynamic uncertainties, local information-based robust controller designed engage model uncertainty compensation. Based on adaptive programming (ADP) algorithm mechanism, obtained by solving Hamilton–Jacobi–Bellman equation (HJBE) with critic neural network (NN). The closed-loop system proved be ultimately uniformly bounded (UUB) using Lyapunov stability theorem. Finally, experimental results illustrate effectiveness developed method.