Distributed Adaptive Neural Consensus Tracking Control for Multiple Euler-Lagrange Systems with Unknown Control Directions. (11th March 2020)

• Record Type: Journal Article
• Authors: Meng, Fanfeng; Zhao, Lin; Yu, Jinpeng
• Other Names: Hafstein Sigurdur F. Academic Editor.
• Is Part Of: Complexity. Volume 2020(2020)
• Publication Date: 2020-03-11
• Subjects: Chaotic behavior in systems -- Periodicals; Complexity (Philosophy) -- Periodicals; 003
• DOI: 10.1155/2020/6061852
• Languages: English
• ISSNs: 1076-2787

Abstract

Abstract : This paper investigates the distributed adaptive neural consensus tracking control for multiple Euler-Lagrange systems with parameter uncertainties and unknown control directions. Motivated by the Nussbaum-type function and command-filtered backstepping technique, the error compensations and neural network approximation-based adaptive laws are established, which can not only overcome the computation complexity problem of backstepping but also make the consensus tracking errors reach to the desired region although the control directions and system nonlinear dynamics are both unknown. Numerical example is given to show the proposed algorithm is effective at last.