Adaptive performance enhancement control for flexible-joint manipulator with model uncertainties and actuator failures

This paper focuses on flexible-joint robotic manipulators (FJRM), which possess numerous advantages such as high flexibility, precision, and fault-tolerance capabilities. However, FJRM are susceptible to various factors that may cause malfunctions during dynamic operations. These malfunctions not only compromise the operational stability and accuracy of the manipulator but also significantly shorten the equipment’s service life. To address these issues, developing an effective control strategy is of significant practical importance. This paper proposes a novel adaptive performance enhancement (APE) control method to effectively tackle model uncertainties and actuator failures in FJRM systems. An adaptive neural network (ANN) algorithm is designed to achieve accurate trajectory tracking of uncertain robotic systems by compensating for modeling errors. A non-singular terminal sliding mode (NTSM) policy is proposed to realize compliance control of robotic manipulators, which enhances the system’s robustness and interference suppression ability. The stability of the closed-loop system is subsequently validated using Lyapunov’s direct method. Finally, the effectiveness of the proposed control method is demonstrated through simulations and experiments conducted on the Gazebo simulation platform and the Baxter robot. Comparative analysis with fuzzy neural network (FNN), neural network (NN) and PD control methods further underscores the superiority of the proposed method in terms of control performance.