Meiqi Tian

Junle Li, Siqi Chen, Jiakai Li et al.

Converting high-level tasks described by natural language into formal specifications like Linear Temporal Logic (LTL) is a key step towards providing formal safety guarantees over cyber-physical systems (CPS). While the compliance of the formal specifications themselves against the safety restrictions imposed on CPS is crucial for ensuring safety, most existing works only focus on translation consistency between natural languages and formal specifications. In this paper, we introduce AutoSafeLTL, a self-supervised framework that utilizes large language models (LLMs) to automate the generation of LTL specifications complying with a set of safety restrictions while preserving their logical consistency and semantic accuracy. As a key insight, our framework integrates Language Inclusion check with an automated counterexample-guided modification mechanism to ensure the safety-compliance of the resulting LTL specifications. In particular, we develop 1) an LLM-as-an-Aligner, which performs atomic proposition matching between generated LTL specifications and safety restrictions to enforce semantic alignment; and 2) an LLM-as-a-Critic, which automates LTL specification refinement by interpreting counterexamples derived from Language Inclusion checks. Experimental results demonstrate that our architecture effectively guarantees safety-compliance for the generated LTL specifications, achieving a 0% violation rate against imposed safety restrictions. This shows the potential of our work in synergizing AI and formal verification techniques, enhancing safety-aware specification generation and automatic verification for both AI and critical CPS applications.

Yihan Liu, Meiqi Tian, teng Yan et al.

Networked control systems (NCS) are widely used in safety-critical applications, but they are often analyzed under the assumption of ideal communication channels. This work focuses on the synthesis of safety controllers for discrete-time linear systems affected by unknown disturbances operating in imperfect communication channels. The proposed method guarantees safety by constructing ellipsoidal robust safety invariant (RSI) sets and verifying their invariance through linear matrix inequalities (LMI), which are formulated and solved as semi-definite programming (SDP). In particular, our framework simultaneously considers controller synthesis and communication errors without requiring explicit modeling of the communication channel. A case study on cruise control problem demonstrates that the proposed controller ensures safety in the presence of unexpected disturbances and multiple communication imperfections simultaneously.

Meiqi Tian, Yihan Liu, Bingzhuo Zhong

Multi-sensor integration via error-state Kalman filter (KF) is widely employed for precise state estimation in cyber-physical systems (CPSs). However, this integration exposes the system to stealthy deception attacks that render conventional detection mechanisms ineffective. We propose an exposure framework to actively reveal such stealthy attacks without modifying sensor interfaces. The framework introduces a suspect mode in which the defender injects random exposure shakes into the nominal control inputs, thus creating a discrepancy between the defender's true state estimates and the attacker's manipulated state estimates, preventing the attack from remaining stealthy. We further derive an explicit exposure condition that characterizes the minimum shake magnitude to guarantee the finite-time exposure and a compensable condition that ensures the shakes do not degrade closed-loop performance. Simulation results based on a GNSS/INS-integrated UAV system verify the effectiveness of the proposed framework.