Neural adaptive coordination control of multiple trains under bidirectional communication topology. Issue 8 (November 2016)

• Record Type: Journal Article
• Authors: Gao, Shigen; Dong, Hairong; Ning, Bin; Roberts, Clive; Chen, Lei
• Is Part Of: Neural computing & applications. Volume 27:Issue 8(2016)
• Page Start: 2497
• Page End: 2507
• Publication Date: 2016-11
• Subjects: Multiple train coordination -- Neural adaptive control -- Bidirectional communication; Neural networks (Computer science) -- Periodicals; Neural circuitry -- Periodicals; Artificial intelligence -- Periodicals; Neural Networks (Computer) -- Periodicals; Réseaux neuronaux (Informatique) -- Périodiques; Réseaux nerveux -- Périodiques; Intelligence artificielle -- Périodiques; 006.32
• DOI: 10.1007/s00521-015-2020-y
• Languages: English
• ISSNs: 0941-0643

Abstract

Abstract This paper investigates the problem of coordination control for a group of trains by neural adaptive approach. The communication structure among trains is a bidirectional one, i.e., necessary information of neighboring trains is used in the control design for a train. Two control schemes are developed, with the first one requiring the information of position, speed, and acceleration of neighboring trains, while the second requiring the information of position of neighboring trains only by virtue of high-order sliding mode observer technique. Based on the universal approximation capacity of radial basis function neural networks, there are no requirements of the precise parameters describing operational resistance and other kinds of extra resistances in the controller design, which are reconstructed by radial basis function neural networks online. The stability of single train and multiple trains are guaranteed by Lyapunov stability theorem. Numerical simulations are presented to demonstrate the effectiveness and performance of the proposed controllers.