Adaptive fuzzy sliding mode control of the manipulator based on an improved super-twisting algorithm

Abstract

The high-precision trajectory tracking and chatter-free performance of the manipulators are of great significance to the improvement of the automation level. Therefore, in view of the problems of chattering and tracking performance degradation of multi-joint manipulators caused by uncertain factors such as external disturbances and modeling errors, an adaptive fuzzy sliding mode control of the manipulator based on an improved super-twisting algorithm is proposed. First, fuzzy control is chosen to approximate the uncertainty of the manipulator model, which reduces the influence of unknown disturbances on the system, and then the adaptive law is utilized to automatically modify and improve the fuzzy rules to enhance the approximation capability; Second, an improved super-twisting algorithm is used on this basis to put the discontinuous term into the integral term to avoid chattering and eliminate the approximation error; Finally, a comparative simulation experiment is carried out with the 3-DOF manipulator as the research object. The experimental results show that the method in this paper improves the convergence speed by up to 69.74% and the tracking error accuracy by up to 85.11% over the fuzzy control based on adaptive reaching law with no chattering and little difference in control inputs, thus verifying the feasibility and superiority of the method.

Underwater manipulator arm control based on Harris Hawk algorithm optimized RBF neural network

“…Its effectiveness was validated through simulations and actual underwater experiments. Chen et al in [8] proposed a robotic arm control strategy that combines adaptive fuzzy control and an improved super-twisting algorithm, enhancing the trajectory tracking accuracy of a threedegree-of-freedom robotic arm and reducing chattering phenomena.…”