Observer-critic-based Event-triggered Decentralized Optimal Control of Modular Robot Manipulators

Abstract

This paper presents a novel observer-critic-based event-triggered decentralized optimal tracking control of modular robot manipulators (MRMs). Based on the dynamic model of the MRM subsystem utilizing joint torque feedback (JTF) technique, the performance index function is constructed by integrating the designed adaptive observer and the tracking error fusion function including position error and velocity error. On the basis of adaptive dynamic programming (ADP) algorithm and the event-triggered mechanism, the decentralized optimal tracking control problem is settled by solving the Hamiltonian-Jacobi-Bellman (HJB) equation via constructing the critic neural network (NN). The closed-loop MRM system is proved to be asymptotically stable by using the Lyapunov stability theorem. Finally, experimental results illustrate the effectiveness of the developed control method.