MohammadHossein Ashoori

Ali Aminzadeh, MohammadHossein Ashoori, Amy Nejati et al.

This paper develops a physics-informed scenario approach for safety verification of nonlinear systems using barrier certificates (BCs) to ensure that system trajectories remain within safe regions over an infinite time horizon. Designing BCs often relies on an accurate dynamics model; however, such models are often imprecise due to the model complexity involved, particularly when dealing with highly nonlinear systems. In such cases, while scenario approaches effectively address the safety problem using collected data to construct a guaranteed BC for the unknown dynamical system, they often require solving an optimization problem with substantial amounts of data. To address this, we propose a physics-informed scenario approach that selects data samples such that the outputs of the physics-based model and the observed data are sufficiently close. This approach guides the scenario optimization process to eliminate redundant samples and potentially reduce the required dataset size. We validate our approach through three case studies, showcasing its practical application in reducing the required data.

MohammadHossein Ashoori, Ali Aminzadeh, Amy Nejati et al.

This work addresses the critical challenge of guaranteeing safety for complex dynamical systems where precise mathematical models are uncertain and data measurements are corrupted by noise. We develop a physics-guided, direct data-driven framework for synthesizing robust safety controllers for discrete-time nonlinear polynomial systems that are subject to unknown-but-bounded disturbances. To do so, we introduce a notion of safety through robust control barrier certificates, which ensure avoidance of unsafe regions, offering a less conservative alternative to existing methods based on robust invariant sets. To achieve data efficiency, we further integrate physical information, formulated as quadratic constraints on system and control matrices, with observed noisy data. This integration drastically reduces data requirements, enabling robust safety analysis with significantly shorter trajectories compared to purely data-driven methods. The proposed synthesis procedure is formulated as a sum-of-squares optimization program that systematically designs the barrier and its associated controller by leveraging both collected data and underlying physical laws. The efficacy of our framework is demonstrated on three benchmark systems, confirming its ability to offer robust safety guarantees with reduced data demands.

Omid Akbarzadeh, MohammadHossein Ashoori, Mohammad H. Mamduhi et al.

This work develops a formal framework for the synthesis of packetized safety controllers for discrete-time polynomial stochastic networked control systems (dt-PSNCS) operating under communication constraints, including uplink delays (plant-to-controller) and downlink packet losses (controller-to-actuator). In this setting, the controller is deployed remotely and exchanges information with the plant over an imperfect wireless communication network. Our proposed approach treats the downlink channel as an erasure channel, with packet losses characterized by an independent Bernoulli process. To systematically manage both uplink delays and downlink packet loss, we first introduce a buffer collocated with the plant that accommodates the packetized safety control (PSC) mechanism. We augment the plant and buffer states into a unified augmented-state representation that accurately captures the system evolution in the presence of communication imperfections. Our proposed framework synthesizes safety controllers based on control barrier certificates (CBCs), providing probabilistic safety guarantees that remain robust in the presence of both communication delays and packet losses. To achieve this, we reformulate the safety constraints as a sum-of-squares (SOS) optimization program, thereby facilitating the systematic construction of CBCs and their corresponding safety controllers. We validate the proposed framework through three (physical) case studies, demonstrating its effectiveness and practical applicability.