On distributed constrained formation control in operator–vehicle adversarial networks

We consider an operator–vehicle network where each vehicle is remotely maneuvered by an operator. The objective of the operators is to steer the vehicles to the desired formation subject to the given state and input constraints. Each operator–vehicle pair is attacked by an adversary who is able to m...

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Veröffentlicht in:	Automatica (Oxford) Jg. 49; H. 12; S. 3571 - 3582
Hauptverfasser:	Zhu, Minghui, Martínez, Sonia
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	Kidlington Elsevier Ltd 01.12.2013 Elsevier
Schlagworte:	Algorithms Applied sciences Asymptotic properties Commands Computer science; control theory; systems Computer systems and distributed systems. User interface Constraints Control theory. Systems Distributed control Exact sciences and technology Memory and file management (including protection and security) Memory organisation. Data processing Multi-agent systems Networks Operators Resilience Resilient control Robotics Software Vehicles Distributed control Resilient control Multi-agent systems Motor car Local network Constrained optimization Resilience Model predictive control Control constraint Multiagent system Distributed algorithm Cryptanalysis Asymptotic approximation Replay attack Denial of service Open ended horizon Formation Computer security State constraint
ISSN:	0005-1098, 1873-2836
Online-Zugang:	Volltext
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Abstract	We consider an operator–vehicle network where each vehicle is remotely maneuvered by an operator. The objective of the operators is to steer the vehicles to the desired formation subject to the given state and input constraints. Each operator–vehicle pair is attacked by an adversary who is able to maliciously replay the control commands sent from the operator. To play against attackers, we come up with a novel distributed resilient algorithm based on the receding-horizon control methodology, and show that the algorithm is able to allow vehicles, on the one hand, satisfy state and input constraints, and on the other hand, asymptotically achieve the desired formation despite replay attacks. With slight modifications, our proposed algorithm shows an analogous resilience to denial-of-service attacks.
AbstractList	We consider an operator-vehicle network where each vehicle is remotely maneuvered by an operator. The objective of the operators is to steer the vehicles to the desired formation subject to the given state and input constraints. Each operator-vehicle pair is attacked by an adversary who is able to maliciously replay the control commands sent from the operator. To play against attackers, we come up with a novel distributed resilient algorithm based on the receding-horizon control methodology, and show that the algorithm is able to allow vehicles, on the one hand, satisfy state and input constraints, and on the other hand, asymptotically achieve the desired formation despite replay attacks. With slight modifications, our proposed algorithm shows an analogous resilience to denial-of-service attacks.
Author	Martínez, Sonia Zhu, Minghui
Author_xml	– sequence: 1 givenname: Minghui surname: Zhu fullname: Zhu, Minghui email: muz16@psu.edu organization: Department of Electrical Engineering, Pennsylvania State University, 201 Old Main, University Park, PA, 16802, USA – sequence: 2 givenname: Sonia surname: Martínez fullname: Martínez, Sonia email: soniamd@ucsd.edu organization: Department of Mechanical and Aerospace Engineering, University of California, San Diego, 9500 Gilman Dr, La Jolla, CA, 92093-0411, USA
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Keywords	Distributed control Resilient control Multi-agent systems Motor car Local network Constrained optimization Resilience Model predictive control Control constraint Multiagent system Distributed algorithm Cryptanalysis Asymptotic approximation Replay attack Denial of service Open ended horizon Formation Computer security State constraint
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Snippet	We consider an operator–vehicle network where each vehicle is remotely maneuvered by an operator. The objective of the operators is to steer the vehicles to... We consider an operator-vehicle network where each vehicle is remotely maneuvered by an operator. The objective of the operators is to steer the vehicles to...
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SubjectTerms	Algorithms Applied sciences Asymptotic properties Commands Computer science; control theory; systems Computer systems and distributed systems. User interface Constraints Control theory. Systems Distributed control Exact sciences and technology Memory and file management (including protection and security) Memory organisation. Data processing Multi-agent systems Networks Operators Resilience Resilient control Robotics Software Vehicles
Title	On distributed constrained formation control in operator–vehicle adversarial networks
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