2021/09/09 by Prithvi Akella, Akella, Prithvi, Ugo Rosolia +3
Computer Science · Engineering · #FOS: Electrical engineering #Fault Detection and Control Systems #Machine Learning and Algorithms #Software Reliability and Analysis Research #Systems and Control (eess.SY) #electronic engineering #information engineering
paper · pdf · doi:10.48550/arxiv.2109.04026
openalex publication_date 2021/09/09 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
As the complexity of control systems increases, the need for systematic methods to guarantee their efficacy grows as well. However, direct testing of these systems is oftentimes costly, difficult, or impractical. As a result, the test and evaluation ideal would be to verify the efficacy of a system simulator and use this verification result to make a statement on true system performance. This paper formalizes that performance translation for a specific class of desired system behaviors. In that vein, our contribution is twofold. First, we detail a variant on existing Bayesian Optimization Algorithms that identifies minimal upper bounds to maximization problems, with some minimum probability. Second, we use this Algorithm to i) lower bound the minimum simulator robustness and ii) upper bound the expected deviance between true and simulated systems. Then, for the specific class of desired behaviors studied, we leverage these bounds to lower bound the minimum true system robustness, without directly testing the true system. Finally, we compare a high-fidelity ROS simulator of a Segway, with a significantly noisier version of itself, and show that our probabilistic verification bounds are indeed satisfied.