Distributed adaptive neural network control for a class of heterogeneous nonlinear multi-agent systems subject to actuation failures

ABSTRACT

In this paper, the cooperative adaptive consensus tracking problem for heterogeneous nonlinear multi-agent systems on directed graph is addressed. Each follower is modelled as a general nonlinear system with the unknown and nonidentical nonlinear dynamics, disturbances and actuator failures. Cooperative fault tolerant neural network tracking controllers with online adaptive learning features are proposed to guarantee that all agents synchronise to the trajectory of one leader with bounded adjustable synchronisation errors. With the help of linear quadratic regulator-based optimal design, a graph-dependent Lyapunov proof provides error bounds that depend on the graph topology, one virtual matrix and some design parameters. Of particular interest is that if the control gain is selected appropriately, the proposed control scheme can be implemented in a unified framework no matter whether there are faults or not. Furthermore, the fault detection and isolation are not needed to implement. Finally, a simulation is given to verify the effectiveness of the proposed method.

KEYWORDS:

• Nonlinear multi-agent system
• actuation failures
• distributed neural network control
• adaptive control

Acknowledgment

The authors thank the anonymous reviewers for their valuable comments and suggestions.

Disclosure statement

No potential conflict of interest was reported by the authors.

Additional information

Funding

This work was supported by the Major State Basic Research Development Program 973 [grant numbers 2012CB215202 and 2014CB249200); the National Natural Science Foundation of China [grant numbers 61134001]; the Basic and Frontier Research Project of Chongqing [grant numbers cstc2013jcyjA70006 and cstc2015jcyjA40038]; the Fundamental Research Funds for the Central Universities [grant number 106112016CDJXY170001].

Notes on contributors

Bing Cui

Bing Cui received his bachelor’s degree in communication engineering from Shandong University, Weihai, in 2011. He is currently working towards his Ph.D. degree in Information and Communication Engineering at Harbin Engineering University. His research interests include cooperative control of multi-agent systems, node or target localisation in wireless sensor networks.

Chunhui Zhao

Chunhui Zhao received his bachelor’s and master’s degree from Harbin Engineering University, in 1986 and 1989, respectively. He received his doctor’s degree from the Department of Automatic Measure and Control, Harbin Institute of Technology, in 1998. He was a postdoctoral research fellow in the College of Underwater Acoustical Engineering, Harbin Engineering University. He is currently a professor in the College of Information and Communication Engineering, Harbin Engineering University. He is a senior member of Chinese Electronics Academy. His research interests include digital signal and image processing, mathematical morphology and nonlinear filters.

Tiedong Ma

Tiedong Ma was born in Liaoning Province, China, in 1978. He received his Ph.D. degree in Control Theory and Control Engineering from Northeastern University, Shenyang, China, in 2009. He is currently an associate professor in the College of Automation, Chongqing University, Chongqing, China. His main research interests are cooperative control of multi-agent systems, impulsive control and synchronisation of complex networks.

Chi Feng

Chi Feng received his master’s degree from Harbin Engineering University. He is currently a professor in the College of Information and Communication Engineering, Harbin Engineering University. His main research interests are computer vision and security system.