Decentralized Tracking Control of Modular Robot Manipulators with Unknown Environmental Contacts Based on Event-triggered Mechanism

Abstract

In this paper, an event-triggered decentralized optimal tracking control method of modular robot manipulators (MRMs) with unknown environmental contacts is proposed. The dynamic model of MRM subsystem is established based on joint torque feedback (JTF) technique. The performance index function is designed including the tracking errors, the model estimation under the proposed radial basis function neural network (RBFNN)-based Romberg observer, and the environmental contacts. On the basis of adaptive dynamic programming (ADP) algorithm and event-triggered mechanism, the corresponding Hamilton-Jacobi-Bellman (HJB) equation is solved by utilizing the critic neural network (NN) structure. Therefore, the event-triggered ADP-based decentralized tracking controller is obtained. Moreover, the closed-loop MRM system is proved to be asymptotically stable using the Lyapunov stability theorem. Finally, the experimental results verified the effectiveness of the proposed controller.