A Modular Safety Filter for Safety-Certified Cyber-Physical Systems
Nowadays, many control systems are networked and embed communication and computation capabilities. Such control architectures are prone to cyber attacks on the cyberinfrastructure. Consequently, there is an impellent need to develop solutions to preserve the plant's safety against potential attacks. To ensure safety, this paper introduces a modular safety filter approach that is effective for various cyber-attack types. This solution can be implemented in combination with existing control and detection algorithms, effectively separating safety from performance. The safety filter does not require information on the received command's reliability or the anomaly detector's feature. It can be implemented in conjunction with high-performance, resilient controllers to achieve both high performance during normal operation and safety during an attack. As an illustrative example, we have shown the effectiveness of the proposed design considering a multi-agent formation task involving 20 mobile robots. The simulation results testify that the safety filter operates effectively during undetectable, intelligent attacks.
Code (0)
등록된 구현이 없습니다.
Similar Papers 제목 키워드 기반
Modular Control Architecture for Safe Marine Navigation: Reinforcement Learning and Predictive Safety Filters
Many autonomous systems face safety challenges, requiring robust closed-loop control to handle physical limitations and safety constraints. Real-world systems, like autonomous ships, encounter nonlinear dynamics and envi…
Perception with Guarantees: Certified Pose Estimation via Reachability Analysis
Agents in cyber-physical systems are increasingly entrusted with safety-critical tasks. Ensuring safety of these agents often requires localizing the pose for subsequent actions. Pose estimates can, e.g., be obtained fro…
Pose EstimationCaRT: Certified Safety and Robust Tracking in Learning-based Motion Planning for Multi-Agent Systems
The key innovation of our analytical method, CaRT, lies in establishing a new hierarchical, distributed architecture to guarantee the safety and robustness of a given learning-based motion planning policy. First, in a no…
Motion PlanningPractical Considerations for Discrete-Time Implementations of Continuous-Time Control Barrier Function-Based Safety Filters
Safety filters based on control barrier functions (CBFs) have become a popular method to guarantee safety for uncertified control policies, e.g., as resulting from reinforcement learning. Here, safety is defined as stayi…
Distributionally Robust Safety Under Arbitrary Uncertainties: A Safety Filtering Approach
We study how to ensure probabilistic safety for nonlinear systems under distributional ambiguity. Our approach builds on a backup-based safety filtering framework that switches between a high-performance nominal policy a…
Computational Efficiency