Event‐based model‐free sliding mode control for an inspection robot. Issue 2 (1st April 2020)

• Record Type: Journal Article
• Authors: Chen, Lin; Duan, Haibin; Dian, Songyi; Hoang, Son
• Other Names: Duan Haibin guestEditor.; Fang Hao guestEditor.
• Is Part Of: Advanced control for applications. Volume 2:Issue 2(2020)
• Page Start: n/a
• Page End: n/a
• Publication Date: 2020-04-01
• Subjects: aperiodically sample -- event‐based model‐free sliding mode control -- event‐triggered mechanism -- inspection robot; Automatic control -- Periodicals; Automatic control; Periodicals; Electronic journals; 629.8
• DOI: 10.1002/adc2.33
• Languages: English
• ISSNs: 2578-0727

Abstract

Abstract : This article investigates the balance adjustment control of a power line inspection robot under the circumstance that (a) only the I/O information of the system is available and (b) the information is sampled aperiodically. To address the problem, an event‐based model‐free sliding mode control algorithm is proposed. The full‐form dynamic linearization (FFDL) technique is first employed to approximate the original nonlinear system via an unknown pseudo‐partial derivative (PPD) Jacobean matrix. Then, an observer is designed to obtain the update law of the PPD matrix using the aperiodically sampled I/O incremental data. Meanwhile, an event‐triggered mechanism is presented to determine when the data can be delivered to the observer. A model‐free sliding mode control framework combined with the established FFDL model, observer, and the event‐triggered mechanism is designed. The stability of the closed‐loop system and the scale of the controller's parameters are confirmed using the Lyapunov theorem. Simulation results demonstrate the effectiveness of the proposed control scheme. Abstract : An event‐based model‐free sliding mode control algorithm is proposed for addressing the balance adjustment control problem of a power line inspection robot under the circumstance that (a) only the I/O information of the system is available and (b) the information is sampled a periodically.