Adaptive Terminal Control of n-DOF Robot Manipulators With Enhanced Nonlinear Model-Based Synergetic Manifold

This paper presents an Adaptive Terminal Model-Based Synergetic Control (ATMB-SC) framework for n-DOF robot manipulators, addressing critical limitations of conventional synergetic control. The proposed strategy integrates a nonlinear model-based manifold function for robotic manipulators, ensuring robust trajectory tracking while eliminating the coupling effect inherent in the conventional synergetic control method. By establishing a linear relationship between the torque input and both position error and its rate of change, the method simplifies control design without compromising robustness or accuracy. An adaptive strategy based on the MIT rule dynamically adjusts the convergence matrix in real-time is implemented, enhancing precision and responsiveness to uncertainties and external disturbances. Additionally, a terminal term is incorporated into the manifold, guaranteeing finite-time convergence of error trajectories, which is critical for high-precision, time-sensitive applications. Experimental validation on a 4-DOF robot demonstrates the strategy’s effectiveness, with input torque variation improved by 10% to 112.67% and the least mean square error achieved across all axes, ranging from $1.63e-4$ to $9.15e-5$ , compared to other controllers. Note to Practitioners—This paper addresses the challenges faced by practicing engineers in the field of robot manipulators, particularly those working in industrial automation and robotics research, such as assembly line automation, material handling, inspection and quality control, etc. The proposed control strategy, known as adaptive terminal model-based synergetic control (ATMB-SC), can be effectively used to enhance the performance of robot manipulator systems, ensuring precise trajectory tracking and efficient energy consumption. The proposed controller integrates a nonlinear manifold function with an adaptive convergence matrix to adjust the real-time performance metrics and reduce the effect of external disturbances and uncertainties. Compared to traditional synergetic control, the proposed strategy significantly improves control precision, reliability, and robustness in unpredictable environments such as automotive manufacturing, aerospace component assembly, and pharmaceutical packaging. Experimental validation using a 4-DOF robotic manipulator demonstrates superior energy efficiency and trajectory precision compared to conventional synergetic control methods. This makes the ATMB-SC method ideal for industrial applications requiring high precision and adaptability under varying conditions.