Global adaptive tracking control of robot manipulators using neural networks with finite-time learning convergence

• Published in: International journal of control, automation, and systems Vol. 15; no. 4; pp. 1916 - 1924
• Main Authors: Yang, Chenguang, Teng, Tao, Xu, Bin, Li, Zhijun, Na, Jing, Su, Chun-Yi
• Format: Journal Article
• Language: English
• Published: Bucheon / Seoul Institute of Control, Robotics and Systems and The Korean Institute of Electrical Engineers 01.08.2017; Springer Nature B.V; 제어·로봇·시스템학회
• Subjects: Adaptive algorithms; Adaptive control; Computer simulation; Control; Convergence; Engineering; Machine learning; Manipulators; Mechatronics; Neural networks; Regular Papers; Robot arms; Robotics; Robust control; Robustness (mathematics); Switching theory; Tracking control; Transient performance; 제어계측공학; robot manipulators; Finite-time learning convergence; globally uniformly ultimate boundedness; neural networks
• ISSN: 1598-6446; 2005-4092
• DOI: 10.1007/s12555-016-0515-7

In this paper, the global adaptive neural control with finite-time (FT) convergence learning performance for a general class of nonlinear robot manipulators has been investigated. The scheme proposed in this paper offers a subtle blend of neural controller with robust controller, which palliates the limitation of neural approximation region to ensure globally uniformly ultimately bounded (GUUB) stability by integrating a switching mechanism. Morever, the proposed scheme guarantees the estimated neural weights converging to optimal values in finite time by embedding an adaptive learning algorithm driven by the estimated weights error. The optimal weights obtained through the learning process of the neural networks (NNs) will be reused next time for repeated tasks, and can thus reduce computational load, improve transient performance and enhance robustness. The simulation studies have been carried out to demonstrate the superior performance of the controller in comparison to the conventional methods.