Collaborative design of fault diagnosis and fault tolerance control under nested signal temporal logic specifications
Penghong Lu, Gang Chen, Rong Su
- Year
- 2025
- Access
- Open access
Abstract
Signal Temporal Logic (STL) specifications play a crucial role in defining complex temporal properties and behaviors in safety-critical cyber-physical systems (CPS). However, fault diagnosis (FD) and fault-tolerant control (FTC) for CPS with nonlinear dynamics remain significant challenges, particularly when dealing with nested signal temporal logic (NSTL) specifications. This paper introduces a novel framework for the collaborative design of FD and FTC, aimed at optimizing fault diagnostic performance while ensuring fault tolerance under NSTL specifications. The proposed framework consists of four key steps: (1) construction of the Signal Temporal Logic Tree (STLT), (2) fault detection via the construction of fault-tolerant feasible sets, (3) evaluation of fault detection performance, and (4) synthesis of fault-tolerant control. Initially, a controller for nonlinear systems is designed to satisfy NSTL specifications, and a fault detection observer is developed alongside fault-tolerant feasible sets. To address the challenge of maintaining solution feasibility in dynamic optimization control problems, the concept of fault-tolerant control recursive feasibility is introduced. Subsequently, suboptimal controller gains are derived through a quadratic programming approach to ensure fault tolerance. The collaborative design framework enables more rapid and accurate fault detection while preserving FTC performance. A simulation study is presented to demonstrate the effectiveness of the proposed framework.
Keywords
Related papers
A dual-loop framework for manufacturability-aware topology optimization of electric vehicle structures via wire arc additive manufacturing
Qiang Cui, Chuan Yu, Daoqian Yang +2 more
Robotics and Computer-Integrated Manufacturing · 2026
Geometric digital twin: A digital and intelligent model for aero-engine assembly accuracy prediction
Ke Shang, Xin Jin, Teli Xu +4 more
Robotics and Computer-Integrated Manufacturing · 2026
Revolutionizing Industries Through AI-Driven Robotics
Aryan Chaudhary
Recent Advances in Computer Science and Communications · 2026
Design and dynamic performance prediction of a novel large-aperture offset-feed deployable antenna
Chuang Shi, Tianming Liu, Ning Xue +6 more
Aerospace Science and Technology · 2026