Adaptive Dynamic Programming-Based Finite-Time Optimal Backstepping Force/Position Control of Reconfigurable Robot Manipulators via Pareto Optimal
Yuexi Wang, Tianjiao An, Bo Lin Dong, Mingchao Zhu, Yuanchun Li · IEEE Transactions on Automation Science and Engineering · 2025
To address the force/position control challenges in transitioning from free-space motion to tasks involving environmental contact, this paper proposes an Adaptive Dynamic Programming (ADP)-based finite-time optimal backstepping force/position control method for Reconfigurable Robot Manipulators (RRMs), which ensures rapid convergence of state errors under external constraints while maintaining system stability. By integrating robust control, the proposed method enhances both convergence speed and robustness against uncertainties. Furthermore, the parameters related to robustness are optimized using a cooperative game-theoretic approach based on Pareto optimality. A Lyapunov-based analysis demonstrates the closed-loop system’s Semi-Global Practical Finite-time Stability (SGPFS). Experimental validation confirms the effectiveness of the proposed control method.