Adaptive neural network control of coordinated robotic manipulators with output constraint. Issue 17 (1st November 2016)

• Record Type: Journal Article
• Authors: Zhang, Shuang; Lei, Minjie; Dong, Yiting; He, Wei
• Is Part Of: IET control theory & applications. Volume 10:Issue 17(2016)
• Page Start: 2271
• Page End: 2278
• Publication Date: 2016-11-01
• Subjects: adaptive control -- neurocontrollers -- manipulators -- uncertainty handling -- stability -- control system synthesis -- radial basis function networks -- function approximation -- Lyapunov methods -- closed loop systems; adaptive neural network control -- coordinated robotic manipulators -- output constraint -- tracking control problem -- uncertainty handling -- instability handling -- controller design -- radial basis function neural network -- bounded function approximation -- continuous function approximation -- barrier Lyapunov function -- stability analysis -- closed‐loop system; Control theory -- Periodicals; Automatic control -- Periodicals; 629.8312
• DOI: 10.1049/iet-cta.2016.0009
• Languages: English
• ISSNs: 1751-8644

Abstract

Abstract : In this study, the authors aim to solve the tracking control problem of coordinated robotic manipulators. In order to handle with the uncertainties and instability of coordinated robotic manipulators and improve the performance of the system with output constraint, they design a controller by using radial basis function neural network which has the ability to approximate any bounded and continuous functions effectively. A barrier Lyapunov function is also introduced to prevent the violation of output constraint. The stability analysis of the closed‐loop system is provided and the performance of the controller is verified through simulation.