A neural network–based sliding mode controller of folding-boom aerial work platform

• Published in: Advances in mechanical engineering Vol. 9; no. 10; p. 168781401772087
• Main Authors: Hu, Haidong, Cai, Ning, Cui, Lizhen, Ren, Yan, Yu, Wensheng
• Format: Journal Article
• Language: English
• Published: London, England SAGE Publications 01.10.2017; Sage Publications Ltd; SAGE Publishing
• Subjects: Boom; Control stability; Control systems design; Controllers; Design; Folding; Hydraulics; International conferences; Neural networks; Performance degradation; Repair & maintenance; Robots; Simulation; Sliding mode control; Switching; Tracking control; Vibration; flexible multi-body dynamics; trajectory tracking control; neural network–based sliding mode control; Aerial work platform
• ISSN: 1687-8132; 1687-8140; 1687-8140
• DOI: 10.1177/1687814017720876

Aerial work platform is a special vehicle for carrying personnel to the appointed site in the air for operations. Therefore, the work platform requires high stability. This article proposes a sliding mode controller based on neural network for tracking control of folding-boom aerial work platform. Since the chattering caused by sliding mode controller with high-speed switching control may lead to system performance degradation, continuous control obtained from neural network system replaces discontinuous switching control to eliminate chattering. Furthermore, the whole system is proved to be stable by Lyapunov stability theorem. Finally, numerical results show that the designed controller can eliminate the chattering resulting from switching control in sliding mode controller and inhibit the vibration of work platform when there exists system uncertainty. Moreover, the controller is effective for the reduction of tracking error.