Attack-resilient observer pruning for path-tracking control of Wheeled Mobile Robot

Abstract

Path-tracking control of wheeled mobile robot (WMR) has gained a lot of research attention, primarily because of its wide applicability -- for example intelligent wheelchairs, exploration-assistant remote WMR. Recent increase in remote and autonomous operations\requirements for WMR has led to more and more use of IoT devices within the control loop. Consequently, providing interfaces for malicious interactions through false data injection attacks (FDIA). Moreover, optimization-based FDIAs have been shown to cause catastrophic consequences in feedback control systems while by-passing any residual-based monitoring system. Since these attacks target system measurement process, this paper focuses on the problem of improving the resiliency of dynamical observers against FDIA. Specifically, we propose an attack-resilient pruning algorithm which attempts to exclude compromised channels from being processed by the observer. The proposed pruning algorithm improves attack-localization precision to $100\%$ with high probability, which correspondingly improves the resiliency of the underlying UKF to FDIA. The improvements due to the developed resilient pruning-based observer is validated through a numerical simulation of a two-layer path-tracking control platform of differential-driven wheeled mobile robot (DDWMR) under FDIA.

arXiv commentASME Dynamic Systems and Control Conference (DSCC 2020)

References in corpus (5)

• Secure estimation and control for cyber-physical systems under adversarial attacks
• Covert Attacks in Cyber-Physical Control Systems
• Exploiting Physical Dynamics to Detect Actuator and Sensor Attacks in Mobile Robots
• Resilient Optimal Estimation Using Measurement Prior
• Robust Resilient Signal Reconstruction under Adversarial Attacks

Cited by in corpus (1)

• Attack-Resilient Weighted ${\ell }_1$ Observer with Prior Pruning