Backstepping Control for Flexible Joint with Friction Using Wavelet Neural Networks and L2‐Gain Approach. Issue 2 (14th July 2017)

• Record Type: Journal Article
• Authors: Chu, Ming; Jia, Qingxuan; Sun, Hanxu
• Is Part Of: Asian journal of control. Volume 20:Issue 2(2018)
• Page Start: 856
• Page End: 866
• Publication Date: 2017-07-14
• Subjects: Backstepping design technique -- flexible joint -- friction compensation -- wavelet neural networks; Automatic control -- Periodicals; Control theory -- Periodicals; 629.805
• DOI: 10.1002/asjc.1602
• Languages: English
• ISSNs: 1561-8625

Abstract

Abstract: In this study, a new backstepping control scheme is proposed to deal with the high accuracy flexible joint servo system's position control. Based on the introduction of non‐consecutive friction, the cascade dynamics equations of flexible joint are established. The macroscopic controller is designed using a backstepping design technique to suppress the flexibility and external disturbance based on the L2 property. To identify the non‐consecutive function, the wavelet neural networks (WNN) are utilized in the microscopic controller to compensate for nonlinear friction and uncertainties. The combined strategy of macro and micro controller can overcome the derivation explosion problem and avoid the joint acceleration measurement and upper bound forecast. Finally, stability analysis and mathematical simulations are presented to verify the effectiveness of this new controller.