Adaptive neural network-based sliding mode control for a hydraulic rotary drive joint. (September 2022)

• Record Type: Journal Article
• Authors: Yang, Mingxing; Zhang, Xing; Xia, Yulei; Liu, Qingyun; Zhu, Qing
• Is Part Of: Computers & electrical engineering. Volume 102(2022)
• Publication Date: 2022-09
• Subjects: Adaptive sliding mode controller -- Higher-order neural network -- Parameter uncertainties -- Position tracking; Computer engineering -- Periodicals; Electrical engineering -- Periodicals; Electrical engineering -- Data processing -- Periodicals; Ordinateurs -- Conception et construction -- Périodiques; Électrotechnique -- Périodiques; Électrotechnique -- Informatique -- Périodiques; Computer engineering; Electrical engineering; Electrical engineering -- Data processing; Periodicals; Electronic journals; 621.302854
• DOI: 10.1016/j.compeleceng.2022.108189
• Languages: English
• ISSNs: 0045-7906

Abstract

Abstract: Hydraulic system has specific nonlinear and unknown modeling characteristics, realizing precise tracking control is a very challenging task. In this study, a sliding mode controller based on an adaptive higher-order neural network is proposed for realizing precise position-tracking control of a hydraulic rotary drive joint. First, the structural design and working principle of the target joint are introduced, and the mathematical model of the corresponding valve-controlled hydraulic position servo system is developed. Then, the adaptive neural network algorithm and sliding mode control are effectively combined. Based on the measurement information obtained from the control process, the feedback error is used to adaptively approximate the control parameters and realize online adjustment of the controller output. Finally, the parameter identification and position tracking of the system are validated, and the results indicate that the proposed strategy exhibits a 20% improvement in position-tracking control compared to the conventional sliding mode control methods. Graphical Abstract: Image, graphical abstract