Adaptive sliding mode control of manipulator based on RBF network minimum parameter learning method. (2nd January 2016)

• Record Type: Journal Article
• Authors: Cui, Yongfeng; Tian, Chong
• Is Part Of: Journal of discrete mathematical sciences & cryptography. Volume 19:Number 1(2016)
• Page Start: 185
• Page End: 197
• Publication Date: 2016-01-02
• Subjects: Adaptive sliding mode control -- RBF network -- Single parameter -- Position tracking curve -- Speed tracking curve; Computer science -- Mathematics -- Periodicals; Cryptography -- Periodicals; Computer science -- Mathematics; Cryptography; Periodicals; 004.0151
• DOI: 10.1080/09720529.2016.1139857
• Languages: English
• ISSNs: 0972-0529

Abstract

Abstract: When using RBF network in approximation, it can achieve a neural network adaptive control without the model, but the algorithm is not convenient in practical control. This paper proposed an adaptive control algorithm based on RBF network minimum parameter learning method for the manipulator. In the algorithm, a single parameter was used to replace the weight values of neural network and no model information is needed. The implementation of adaptive control based on single parameter estimation can be achieved with this algorithm. The simulation results show that the presented control algorithm has good tracking performance and real-time capability.