9,540 research outputs found
Investigating Robustness in Cyber-Physical Systems: Specification-Centric Analysis in the face of System Deviations
The adoption of cyber-physical systems (CPS) is on the rise in complex
physical environments, encompassing domains such as autonomous vehicles, the
Internet of Things (IoT), and smart cities. A critical attribute of CPS is
robustness, denoting its capacity to operate safely despite potential
disruptions and uncertainties in the operating environment. This paper proposes
a novel specification-based robustness, which characterizes the effectiveness
of a controller in meeting a specified system requirement, articulated through
Signal Temporal Logic (STL) while accounting for possible deviations in the
system. This paper also proposes the robustness falsification problem based on
the definition, which involves identifying minor deviations capable of
violating the specified requirement. We present an innovative two-layer
simulation-based analysis framework designed to identify subtle robustness
violations. To assess our methodology, we devise a series of benchmark problems
wherein system parameters can be adjusted to emulate various forms of
uncertainties and disturbances. Initial evaluations indicate that our
falsification approach proficiently identifies robustness violations, providing
valuable insights for comparing robustness between conventional and
reinforcement learning (RL)-based controllersComment: 12 page
Closed-Loop Statistical Verification of Stochastic Nonlinear Systems Subject to Parametric Uncertainties
This paper proposes a statistical verification framework using Gaussian
processes (GPs) for simulation-based verification of stochastic nonlinear
systems with parametric uncertainties. Given a small number of stochastic
simulations, the proposed framework constructs a GP regression model and
predicts the system's performance over the entire set of possible
uncertainties. Included in the framework is a new metric to estimate the
confidence in those predictions based on the variance of the GP's cumulative
distribution function. This variance-based metric forms the basis of active
sampling algorithms that aim to minimize prediction error through careful
selection of simulations. In three case studies, the new active sampling
algorithms demonstrate up to a 35% improvement in prediction error over other
approaches and are able to correctly identify regions with low prediction
confidence through the variance metric.Comment: 8 pages, submitted to ACC 201
- …