A distributed adaptive architecture with the nonlinear reference model for safe finite-time control of uncertain multiagent systems

ABSTRACT

We propose a new distributed adaptive control architecture for finite-time control of uncertain nonlinear multiagent systems. The proposed architecture employs three key components for each agent; a nonlinear reference model, a weight update rule, and an adaptive control signal. Predicated on agent-wise reference model state exchange, an ideal finite-time behaviour of overall multiagent system is captured by nonlinear reference models. The weight update rule of each agent, which is driven by a local error signal between the actual uncertain state of an agent and its reference model state, then adjusts agent-wise controller parameters in real-time to drive the local error signals of each agent to zero in finite-time. That is, not only the states of agents converge to their nonlinear reference model states in finite-time, but also the latter states converge to the given ideal behaviour in finite-time. Considering safety, the distinct feature of our architecture is that it does not rely on agent-wise actual state exchange between agents, which involves the effect of system uncertainties. This implies that when a subset of agents exhibits, for example, Byzantine behaviour, then their behaviour do not affect the rest of multiagent system from functioning.

KEYWORDS:

• Finite-time control
• multiagent systems
• distributed control
• adaptive control

Acknowledgments

The first author would like to thank the Republic of Turkey Ministry of National Education for their support on this research.

Disclosure statement

No potential conflict of interest was reported by the author(s).

Data availability statement

The data that support the findings of the numerical results are available from the authors upon reasonable request.

Additional information

Notes on contributors

Meryem Deniz

Meryem Deniz received her Master of Science degree in the Department of Electrical and Electronics Engineering at the Izmir Institute of Technology, Turkey, in 2015. Then, she received her Ph.D. degree in the Mechanical Engineering Department at Missouri University of Science and Technology, Rolla, MO, USA, in 2022. She is currently a Postdoctoral Research Associate of the Department of Electrical Engineering at the University of Texas at Arlington, Arlington, TX, USA. Prior to joining the University of Texas at Arlington, she held a Research/Teaching Assistant position in the Department of Mechanical Engineering at Missouri University of Science and Technology. Her research interests include UAV traffic management, adaptive control of uncertain nonlinear systems, optimal control, and control techniques for mechatronics, and Biosystems.

K. Merve Dogan

Dr. K. Merve Dogan is an Assistant Professor of the Department of Aerospace Engineering, the director of the Foundational Autonomous Systems and Technologies (FAST) Research Group at the Embry-Riddle Aeronautical University since August 2020. Prior to joining the Embry-Riddle Aeronautical University, she held the Research Assistant position in the Department of Mechanical Engineering at the University of South Florida between 2015 and 2020, where she received her Doctor of Philosophy degree in 2020. Before joining the University of South Florida, she held a Research/Teaching Assistant position in the Department of Electrical and Electronics Engineering at the Izmir Institute of Technology between 2012 and 2015, where she received her Master of Science degree in 2016. Dr. Dogan is a Co-Director of the Forum on Robotics and Control Engineering (FoRCE), and is a member of AIAA and IEEE, including several technical committees.

Tansel Yucelen

Tansel Yucelen received the Ph.D. degree in aerospace engineering from the Georgia Institute of Technology, Atlanta, GA, USA, in 2012. From 2011 to 2013, he held research engineer positions at the Georgia Institute of Technology. From 2013 to 2016, he was an Assistant Professor with the Missouri University of Science and Technology, Rolla, MO, USA; and from 2016 to 2020, he was an Assistant Professor with the University of South Florida, Tampa, FL, USA. He is currently an Associated Professor with the Department of Mechanical Engineering and the Director of the Laboratory for Autonomy, Control, Information, and Systems, University of South Florida, Tampa, FL, USA. His research interests include adaptive and robust control of safety-critical systems; distributed estimation and control of networked multiagent systems; resilient and secure robotics, autonomous vehicles, and cyber-physical systems; and large-scale and modular systems. Dr. Yucelen is a member of the National Academy of Inventors, an Associate Fellow of the AAA, and a Senior Member of the IEEE.