Adaptive Predefined Performance Neural Control for Robotic Manipulators with Unknown Dead Zone. (12th May 2020)

• Record Type: Journal Article
• Authors: Shao, Shifen; Zhang, Kaisheng; Li, Jun; Wang, Jirong
• Other Names: Na Jing Guest Editor.
• Is Part Of: Mathematical problems in engineering. Volume 2020(2020)
• Publication Date: 2020-05-12
• Subjects: Engineering mathematics -- Periodicals; 510.2462
• DOI: 10.1155/2020/6490167
• Languages: English
• ISSNs: 1024-123X

Abstract

Abstract : This paper proposes an adaptive predefined performance neural control scheme for robotic manipulators in the presence of nonlinear dead zone. A neural network (NN) is utilized to estimate the model uncertainties and unknown dynamics. An improved funnel function is designed to guarantee the transient behavior of the tracking error. The proposed funnel function can release the assumption on the conventional funnel control. Then, an adaptive predefined performance neural controller is proposed for robotic manipulators, while the tracking errors fall within a prescribed funnel boundary. The closed-loop system stability is proved via Lyapunov function. Finally, the numerical simulation results based on a 2-DOF robotic manipulator illustrate the control effect of the presented approach.