Masters Theses

Keywords and Phrases

Attack detection; Attack estimation; Autonomous systems; Formation control; Nonholonomic system

Abstract

"A formation of cheap and agile robots can be deployed for space, mining, patrolling, search and rescue applications due to reduced system and mission cost, redundancy, improved system accuracy, reconfigurability, and structural flexibility. However, the performance of the formation can be altered by an adversary. Therefore, this thesis investigates the effect of adversarial inputs or attacks on a nonholonomic leader-follower-based robot formation and introduces novel detection and mitigation schemes.

First, an observer is designed for each robot in the formation in order to estimate its state vector and to compute the control law. Based on the healthy operation of the robot and its formation, it has been shown that in the case of false data injection (FDI) attack on the actuator of a robot, the state estimation error or residual increases thus indicating the onset of an attack. Next, a functional link neural network is incorporated into the observer to learn the attack input and to minimize its effect by modifying the controller.

Subsequently, the effects of a covert attack are studied by relaxing the assumption that sensors are attack-resilient. It is shown that the residual-based method from Paper 1 is ineffective when the sensors are injected by a signal that modifies the residual in the presence of an actuator attack. Next, an auxiliary system consisting of an observer for each robot, which is not known to the adversary, is introduced to detect covert attacks.

Performance assurance and stability of the formation during healthy and under attack are shown using Lyapunov analysis by relaxing the separation principle. Simulation results verify theoretical results for both FDI and covert attacks"--Abstract, page iv.

Advisor(s)

Sarangapani, Jagannathan, 1965-
Modares, Hamidreza

Committee Member(s)

Zawodniok, Maciej Jan, 1975-

Department(s)

Electrical and Computer Engineering

Degree Name

M.S. in Electrical Engineering

Publisher

Missouri University of Science and Technology

Publication Date

Spring 2020

Journal article titles appearing in thesis/dissertation

  • Actuator attack detection and mitigation in a dynamic mobile robot formation
  • Covert attack detection in a dynamic mobile robot formation

Pagination

x, pages

Note about bibliography

Includes bibliographic references.

Rights

© 2020 Arnold Fernandes, All rights reserved.

Document Type

Thesis - Open Access

File Type

text

Language

English

Thesis Number

T 11674

Electronic OCLC #

1164095802

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