Decentralized Tracking Control for Modular Reconfigurable Robots Using Data-Based Concurrent Learning

Abstract

This paper proposes a decentralized tracking control (DTC) through data-based concurrent learning for modular reconfigurable robots (MRRs) with unknown dynamics. By using the local input-output data and reference trajectories of interconnected subsystems, a neural network (NN)-based local observer is established to acquire the MRR dynamics online. Based on the adaptive dynamic programming algorithm, the local Hamilton- Jacobi-Bellman equation is solved by a local critic NN, whose weight vector is tuned by a concurrent learning-based updating law. Then, the DTC policies are obtained, and the persistence of excitation condition is removed. The tracking error of the entire closed-loop MRR system is guaranteed to be uniformly ultimately bounded by the Lyapunov’s direct method. The simulation on a 2- DOF MRR system demonstrates that the proposed DTC scheme is effective.