Technology Category
Constraint Satisfaction and Optimization: Distributed CSP/OptimizationReasoning under Uncertainty: Stochastic OptimizationMachine Learning: Calibration & Uncertainty Quantification
Reachset-Conformant System Identification
🤖 AI Summary
This paper addresses the automated identification of consistency between dynamic models and empirical measurements in cyber-physical system (CPS) safety verification—specifically, verifying whether their respective reachable sets coincide. Method: We propose a unified optimization framework driven by reachable-set verification, integrating interval analysis, robust optimization, and data-driven modeling to jointly identify parametric and structural model uncertainties. Our approach generalizes reachable-set consistency identification from linear state-space models to nonlinear state-space and input–output models, supporting white-box, gray-box, and black-box modeling paradigms under varying levels of prior dynamical knowledge. Results: Evaluated on both synthetic and real-world CPS datasets, the method significantly improves coverage of safety-critical behaviors and ensures reliable transfer of formal verification results to physical implementations.
Technology Category
Application Category
📝 Abstract
Formal verification techniques play a pivotal role in ensuring the safety of complex cyber-physical systems. To transfer model-based verification results to the real world, we require that the measurements of the target system lie in the set of reachable outputs of the corresponding model, a property we refer to as reachset conformance. This paper is on automatically identifying those reachset-conformant models. While state-of-the-art reachset-conformant identification methods focus on linear state-space models, we generalize these methods to nonlinear state-space models and linear and nonlinear input-output models. Furthermore, our identification framework adapts to different levels of prior knowledge on the system dynamics. In particular, we identify the set of model uncertainties for white-box models, the parameters and the set of model uncertainties for gray-box models, and entire reachset-conformant black-box models from data. The robustness and efficacy of our framework are demonstrated in extensive numerical experiments using simulated and real-world data.
Problem
Research questions and friction points this paper is trying to address.
Automatically identify reachset-conformant models for cyber-physical systems.
Generalize identification methods to nonlinear and input-output models.
Adapt framework to various levels of system dynamics knowledge.
Innovation
Methods, ideas, or system contributions that make the work stand out.
Generalizes reachset-conformant identification to nonlinear models
Adapts to varying prior knowledge on system dynamics
Identifies model uncertainties and parameters from data
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