Deep Deterministic Policy Gradient-Based Parameter Adaptation for Synchronous Sliding-Mode Control with Time-Delay Estimation in Dual-Arm Robot Manipulators Under System Uncertainties

This paper presents a synchronous sliding-mode control with time-delay estimation (SSMC-TDE)-based adaptive control framework for coordinated motion control of dual-arm robotic manipulators operating under system uncertainties. The baseline SSMC-TDE scheme is constructed using synchronization and cross-coupling errors to ensure precise coordinated motion among robot joints, while sliding-mode control effectively handles strong nonlinearities, and the time-delay estimation technique approximates lumped uncertainties arising from external disturbances, modeling errors, and payload variations. The stability of the closed-loop system is rigorously analyzed and guaranteed using the Lyapunov theory. To overcome performance degradation caused by manually tuned control gains, a deep reinforcement learning-assisted parameter adaptation mechanism is integrated into the SSMC-TDE structure. Specifically, a Deep Deterministic Policy Gradient (DDPG) algorithm is employed to adapt selected control gains online through a reward function designed to simultaneously enhance motion synchronization and reduce trajectory-tracking errors, while preserving the stability properties of the underlying controller. Simulation studies are conducted within a co-simulation framework integrating MATLAB/Simulink and ROS/Gazebo for a dual-arm robotic platform. Quantitative evaluations based on the root mean square error (RMSE) of trajectory-tracking and synchronization errors across all six joints demonstrate that, averaged over both scenarios, the proposed DDPG-assisted SSMC-TDE achieves an overall RMSE reduction of 35.52% and 99.3% compared with conventional SSMC and SSMC-TDE controllers, respectively, confirming its superior performance and robustness under system uncertainties.