High-Order Sliding Mode Control for Three-Joint Rigid Manipulators Based on an Improved Particle Swarm Optimization Neural Network

Jin Zhang, Wenjun Meng (), Yufeng Yin, Zhengnan Li, Lidong Ma and Weiqiang Liang
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Jin Zhang: School of Mechanical Engineering, Taiyuan University of Science and Technology, Taiyuan 030024, China
Wenjun Meng: School of Mechanical Engineering, Taiyuan University of Science and Technology, Taiyuan 030024, China
Yufeng Yin: School of Mechanical Engineering, Taiyuan University of Science and Technology, Taiyuan 030024, China
Zhengnan Li: School of Mechanical Engineering, Taiyuan University of Science and Technology, Taiyuan 030024, China
Lidong Ma: School of Mechanical Engineering, Taiyuan University of Science and Technology, Taiyuan 030024, China
Weiqiang Liang: School of Mechanical Engineering, Taiyuan University of Science and Technology, Taiyuan 030024, China

Mathematics, 2022, vol. 10, issue 19, 1-22

Abstract: This paper presents a control method for the problem of trajectory jitter and poor tracking performance of the end of a three-joint rigid manipulator. The control is based on a high-order particle swarm optimization algorithm with an improved sliding mode control neural network. Although the sliding mode variable structure control has a certain degree of robustness, because of its own switching characteristics, chattering can occur in the later stage of the trajectory tracking of the manipulator end. Hence, on the basis of the high-order sliding mode control, the homogeneous continuous control law and super-twisting adaptive algorithm were added to further improve the robustness of the system. The radial basis function neural network was used to compensate the errors in the modeling process, and an adaptive law was designed to update the weights of the middle layer of the neural network. Furthermore, an improved particle swarm optimization algorithm was established and applied to optimize the parameters of the neural network, which improved the trajectory tracking of the manipulator end. Finally, MATLAB simulation results indicated the validity and superiority of the proposed control method compared with other sliding mode control algorithms.

Keywords: neural network; particle swarm optimization algorithm; sliding mode control; super-twisting adaptive algorithm; manipulators control (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2022
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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