Robust output containment control of multi-agent systems with unknown heterogeneous nonlinear uncertainties in directed networks

ABSTRACT

Output containment problem for high-order nonlinear time-invariant multi-agent systems in directed networks is investigated in this paper. The output is related with the observation matrix. The dimensions of observation matrix are extended so that it is non-singular. Then the containment problem is transformed into stability problem. The model of each agent is constructed by a nominal system combined with uncertainties. A robust controller, which includes a nominal controller and a robust compensator, is proposed to achieve output containment and restrain external uncertainties. The nominal controller is based on the output feedback and the nominal system constructed by the nominal controller contains desired containment properties. The robust compensator design is based on robust signal compensation technology for restraining the effects of external disturbances. A sufficient condition on the output containment is proposed and the containment errors can be made as small as desired with the expected convergence rate. Finally, numerical simulation is presented to demonstrate the effectiveness of the control method.

KEYWORDS:

• Output containment
• nonlinear uncertainties
• output feedback
• robust controller

Acknowledgements

The authors would like to thank the associate editor and the anonymous reviewers for their detailed comments and suggestions.

Disclosure statement

No potential conflict of interest was reported by the authors.

Additional information

Funding

This work was supported by the National Natural Science Foundation [grant number 61473324, 61520106009, 61533008], Fundamental Research Funds for the Central Universities [grant number FRF-TP-15-058A3], Beijing Natural Science Foundation under Grant [grant number 4154079].

Notes on contributors

Changyin Sun

Changyin Sun Received his bachelors degree with the College of Mathematics, Sichuan University, Chengdu, China, in 1996, and the M.S. and Ph.D. degrees in electrical engineering from the Southeast University, Nanjing, China, in 2001 and 2003, respectively. He is a distinguished Professor with the School of Automation and Electrical Engineering, University of Science and Technology Beijing, Beijing, China and the School of Automation, Southeast University, Nanjing, China. His current research interests include intelligent control, flight control, pattern recognition, and optimal theory. Prof. Sun is an Associate Editor of the IEEE Transactions on Neural Networks and Learning Systems.

Qing Wang

Qing Wang Received her B.Eng. degree from the School of Automation and Electrical Engineering, University of Science and Technology Beijing, Beijing, China, in 2013. She is a Ph.D. candidate in School of Automation and Electrical Engineering, University of Science and Technology Beijing. Her current research interests include multi-agent systems, nonlinear systems, robust control and intelligent control.

Yao Yu

Yao Yu Received her B.Sc. degree from Department of Control Science and Engineering, Huazhong University of Science and Technology in 2004, the M.S. and Ph.D. degrees from Department of Automation, Tsinghua University in 2010. She was a postdoctor with Tsinghua University. Currently, she is an Associate Professor with the School of Automation and Electrical Engineering, University of Science and Technology Beijing. Her current research interests include nonlinear control, robust control and time-delay systems.