Identification Recurrent Type 2 Fuzzy Wavelet Neural Network and L2‐Gain Adaptive Variable Sliding Mode Robust Control of Electro‐Hydraulic Servo System (EHSS). Issue 4 (25th September 2017)

• Record Type: Journal Article
• Authors: Chen, Xiangjian; Li, Di; Yang, Xibei; Yu, Yuecheng
• Is Part Of: Asian journal of control. Volume 20:Issue 4(2018)
• Page Start: 1480
• Page End: 1490
• Publication Date: 2017-09-25
• Subjects: Electro‐hydraulic servo system (EHSS) nonlinear system -- recurrent type 2 fuzzy wavelet neural network -- L2 gain adaptive variable sliding mode control -- robust control; Automatic control -- Periodicals; Control theory -- Periodicals; 629.805
• DOI: 10.1002/asjc.1643
• Languages: English
• ISSNs: 1561-8625

Abstract

Abstract: An electro‐hydraulic servo system (EHSS) is a kind of system with the characteristics of time‐variant, serious nonlinearity, parameter and structural uncertainty, and uncertain load disturbance in most cases. These characteristics make it very difficult to realize highly accurate control by conventional methods. In order to solve the above problems, this paper introduces a recurrent type 2 fuzzy wavelet neural network to approximate the unknown nonlinear functions of the dynamic systems through tuning by the desired adaptive law. Based on the identification by recurrent type 2 fuzzy wavelet neural network, a L2 gain design method, combining gain adaptive variable sliding mode control with H infinity control, is proposed for load disturbance, thereby accommodating uncertainties that are the main factors affecting system stability and accuracy in EHSS. In this algorithm, a recurrent type 2 fuzzy wavelet neural network is employed to evaluate the unknown dynamic characteristics of the system and gain adaptive variable sliding mode control to compensate for evaluating errors, and H infinity control to suppress the effect on system by load disturbance. The experiment results show that the proposed system L2 gain design method can make the system exhibit strong robustness to parameter variation and load disturbance.