Neural network-based adaptive consensus tracking control for multi-agent systems under actuator faults

Abstract

In this paper, a distributed output feedback consensus tracking control scheme is proposed for second-order multi-agent systems in the presence of uncertain nonlinear dynamics, external disturbances, input constraints, and partial loss of control effectiveness. The proposed controllers incorporate reduced-order filters to account for the unmeasured states, and the neural networks technique is implemented to approximate the uncertain nonlinear dynamics in the synthesis of control algorithms. In order to compensate the partial loss of actuator effectiveness faults, fault-tolerant parts are included in controllers. Using the Lyapunov approach and graph theory, it is proved that the controllers guarantee a group of agents that simultaneously track a common time-varying state of leader, even when the state of leader is available only to a subset of the members of a group. Simulation results are provided to demonstrate the effectiveness of the proposed consensus tracking method.

Keywords:

• multi-agent systems
• consensus tracking control
• neural network (NN)
• input constraints
• actuator faults
• output feedback

Acknowledgements

The authors would like to thank the anonymous reviewers for their constructive comments and valuable suggestions which greatly improved the presentation of this article.

Additional information

Funding

This work was supported by the National Basic Research Program of China (973 Program) [grant number 2012CB821200], [grant number 2012CB821201]; the NSFC [grant number 61134005], [grant number 61221061], [grant number 61327807].

Notes on contributors

Lin Zhao

Lin Zhao was born in Shandong, China. He received his BS degree in mathematics and applied mathematics from Qingdao University, Qingdao, China, in 2008, and his MS degree in operational research and cybernetics from Ocean University of China, Qingdao, China, in 2011. Since 2011, he has been working towards his PhD degree in applied mathematics at Beihang University (BUAA), Beijing, China. His current research interests include spacecraft control and distributed control of multi-agent systems.

Yingmin Jia

Yingmin Jia received his BS in control theory from Shandong University, Ji'nan, China, in January 1982, and his MS and PhD both in control theory and applications from Beihang University (BUAA), Beijing, China, in 1990 and 1993, respectively. In 1993, he joined the Seventh Research Division at Beihang University, where he is currently a professor of automatic control. From February 1995 until February 1996, he was a visiting professor with the Institute of Robotics and Mechatronics of the German Aerospace Center (DLR), Oberpfaffenhofen, Germany. He held an Alexander von Humboldt (AvH) research fellowship with the Institute of Control Engineering at the Technical University Hamburg-Harburg, Hamburg, Germany, from December 1996 until March 1998, and a JSPS research fellowship with the Department of Electrical and Electronic Systems at the Osaka Prefecture University, Osaka, Japan, from March 2000 until March 2002. He was a visiting professor with the Department of Statistics at the University of California Berkeley from December 2006 until March 2007. His current research interests include robust control, adaptive control and intelligent control, and their applications in industrial processes and vehicle systems. He is author and co-author of numerous papers and of the book ‘Robust H_∞ Control’ (Science Press 2007).