A Backstepping Controller with the RBF Neural Network for Folding-Boom Aerial Work Platform. (20th January 2022)

• Record Type: Journal Article
• Authors: Hu, Haidong; Song, Yandong; Fan, Pu; Diao, Chen; Cai, Ning
• Other Names: Selişteanu Dan Academic Editor.
• Is Part Of: Complexity. Volume 2022(2022)
• Publication Date: 2022-01-20
• Subjects: Chaotic behavior in systems -- Periodicals; Complexity (Philosophy) -- Periodicals; 003
• DOI: 10.1155/2022/4289111
• Languages: English
• ISSNs: 1076-2787

Abstract

Abstract : Aerial work platform is a kind of engineering vehicle which is used for hoisting personnel to the appointed place for maintenance or installation. Based on the dynamics model considering the flexible deformation existing in the arm system of folding-boom aerial platform vehicle, this study presents a NN-based backstepping controller used for trajectory tracking control of work platform. The proposed controller can reduce tracking error of work platform and suppress the vibration simultaneously by using the RBF neural network system to compensate model uncertainties and disturbances. Furthermore, we prove that the whole system is stable and convergent by Lyapunov stability theorem. In addition, we give the simulation results which show that the good control performance of the designed controller for trajectory tracking and vibration inhabiting of work platform in the case of model uncertainties.