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Safe Control for Nonlinear Systems Under Faults and Attacks Via Control Barrier Functions
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Safety is one of the most important properties of control systems. Sensor faults and attacks and actuator failures may cause errors in the sensor measurements and system dynamics, which leads to erroneous control inputs and hence safety violations. In this paper, we improve the robustness against sensor faults and actuator failures by proposing a class of Fault-Tolerant Control Barrier Functions (FT-CBFs) for nonlinear systems. Our approach maintains a set of state estimators according to fault patterns and incorporates CBF-based linear constraints for each state estimator. We then propose a framework for joint safety and stability by integrating FT-CBFs with Control Lyapunov Functions. With a similar philosophy of utilizing redundancy, we proposed High order CBF-based approach to ensure safety when actuator failures occur. We propose a sum-of-squares (SOS) based approach to verify the feasibility of FT-CBFs for both sensor faults and actuator failures. We evaluate our approach via two case studies, namely, a wheeled mobile robot (WMR) system in the presence of a sensor attack and a Boeing 747 lateral control system under actuator failures.
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Cited by 2 Pith papers
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A secure safety filter combines secure state reconstruction under bounded noise with a robust control barrier function QP, and tests on a quadrotor show it can keep the drone safe when one sensor stream is spoofed.
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Secure Safety Filter Design for Sampled-data Nonlinear Systems under Sensor Spoofing Attacks
Secure safety filters for sampled-data nonlinear systems under sensor spoofing are constructed from exact and relaxed observability maps, with safety guaranteed under sparse-observability and control barrier function ...
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