Adaptive Neural Network Vibration Control for an Output-Tension-Constrained Axially Moving Belt System With Input Nonlinearity

• Published in: IEEE/ASME transactions on mechatronics Vol. 27; no. 2; pp. 656 - 665
• Main Authors: Liu, Yu, Liu, Fengjiao, Mei, Yanfang, Wan, Weiwei
• Format: Journal Article
• Language: English
• Published: New York IEEE 01.04.2022; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Acceleration; Adaptive control; Adaptive neural network control; Adaptive systems; asymmetric constraint; axially moving system; Belt conveyors; Belts; Constraints; Deceleration; Feedback control; Liapunov functions; Lyapunov methods; Neural networks; Nonlinearity; Production; Radial basis function; S curves; Vibration control; Vibrations
• ISSN: 1083-4435; 1941-014X
• DOI: 10.1109/TMECH.2021.3126686

In this article, to suppress the vibration of an axially moving belt system of surface-mounted technology, which is working with S-curve acceleration/deceleration, an adaptive neural network controller is proposed utilizing backstepping method and Lyapunov's theory. Considering input nonlinearity, external disturbance, and system uncertainty, radial basis function (RBF) neural networks are adopted to eliminate the effect of these uncertain terms. Besides, in order to ensure the production quality of the equipment and for the sake of safety, both the deformation constraint and tension constraint are taken into account, and a barrier Lyapunov function is employed to guarantee the restrictions. The stability of the closed-loop system is proved and simulations are given to illustrate the well performance of the proposed control strategy.