Control of misbehaving multiagent networks using driver and observer nodes

Abstract

While applying control signals to every node is typically considered in multiagent networks to mitigate the adverse effects of misbehaving nodes, this control strategy may not always be possible due to physical constraints and/or economical limitations. Motivated by this standpoint, we have recently focused on how to control misbehaving multiagent networks through sending control signals to a subset of nodes (i.e. driver nodes) for suppressing the adverse effects of misbehaving nodes in the overall multiagent network, where the driver nodes only use their own state information to generate their control signals. To address the open problem where the driver nodes cannot obtain their own state information, the main contribution of this paper is that we now take into account that these nodes need to generate their control signals using the state information received from other subset of nodes (i.e. observer nodes). We characterise which nodes in the network behave as desired based on the selection of driver nodes and observer nodes in control of misbehaving multiagent networks. In addition, we provide numerical examples to illustrate the effectiveness of the proposed approach as well as the importance of the selection of driver and observer nodes for maximising the number of nodes that exhibit the desired responses.

Keywords:

• Multiagent networks
• misbehaving nodes
• driver nodes
• observer nodes
• system-theoretical analyses

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

Funding

This research was supported by the National Science Foundation under Grant CMMI-1657637.

Notes on contributors

Emre Yildirim

Emre Yildirim received a B.S. degree in mechanical engineering from the Yildiz Technical University, Istanbul, Turkey, in 2013. He is currently pursuing a Ph.D. degree in mechanical engineering with the University of South Florida, Tampa, FL, USA. His current research includes multiagent systems with applications to aerial swarms and ground robots.

Alexander Saltos

Alexander Saltos is a Ph.D. student at the University of South Florida in Tampa, USA, specialising in mechanical engineering. He holds an M.S.M.E. from the same institution and also serves as an adjunct professor for the College of Engineering. His current research centres around multiagent systems and error mitigation processes.

Tansel Yucelen

Tansel Yucelen received a 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 at the 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 and a senior member of the IEEE and the AIAA.