Zero-sum game-based neuro-optimal control of modular robot manipulators with uncertain disturbance using critic only policy iteration

In this paper, a zero-sum differential game strategy-based neuro-optimal control method is presented via critic only policy iteration-adaptive dynamic programming (COPI-ADP) approach to address optimal trajectory tracking control problem of modular robot manipulators (MRMs) with uncertain disturbance. The dynamic model of modular robot manipulator systems is formulated as an integration of joint subsystems and unknown robotic model uncertainties are identified by the developed linear extension state observer. Then, the optimal control issue of the modular robot manipulator systems with uncertain disturbance is transformed into a two-player zero-sum differential game one. Based on adaptive dynamic programming and policy iteration algorithms, the Hamilton-Jacobi-Issacs (HJI) equation is approximately solved using only critic neural network and thus facilitating the feasible derivation of the approximated optimal control policy. The trajectory of tracking errors of modular robot manipulator system is guaranteed to be uniform ultimate bounded by using the Lyapunov theory. Finally, experiments are provided to demonstrate the advantage and effectiveness of the developed control method. (c) 2021 Elsevier B.V. All rights reserved.

Automated summary

This study introduces a novel neuro-optimal control method based on game theory and dynamic programming to solve the optimal trajectory tracking control problem of modular robot manipulators, ensuring bounded tracking errors and demonstrating the advantage and effectiveness of the control method through experiments.