arXiv subjects

Mehran Attar

Publications and source records attributed to Mehran Attar.

6 recordsLinked to original sources

arXiv2026

Efficient Approximation of the Wigner Kernel in Phase-Space Quantum Mechanics

The Signed Particle Formulation provides a particle-based interpretation of quantum mechanics in phase space, where quantum dynamics are represented through the creation and evolution of signed particles. A central computational challenge in this framework is the evaluation of the Wigner kernel, which generally involves highly oscillatory integrals and can become computationally demanding in time-dependent simulations. This paper proposes an analytical approximation of the Wigner kernel for one-dimensional single body quantum systems by exploiting a series-based representation of the potential function. The resulting expression provides an efficient way to approximate the Wigner kernel and the associated Gamma function, which governs the particle-generation process in the Signed Particle Formulation framework. The proposed approximation is evaluated for several Gaussian-based potential profiles, including single, double, triple, and quadruple Gaussian potentials. Numerical comparisons between the approximated and directly computed Wigner kernels and Gamma functions show that the proposed method captures the main behavior of the exact quantities while significantly reducing the computational cost. These results indicate that the proposed approximation can serve as an efficient computational component for scalable Signed Particle Formulation based quantum simulations.

quant-ph↗

arXiv2024

A Data-Driven Approach To Preserve Safety and Reference Tracking for Constrained Cyber-Physical Systems Under Network Attacks

This paper proposes a worst-case data-driven control architecture capable of ensuring the safety of constrained Cyber-Physical Systems under cyber-attacks while minimizing, whenever possible, potential degradation in tracking performance. To this end, a data-driven robust anomaly detector is designed to detect cyber-attack occurrences. Moreover, an add-on tracking supervisor module allows safe open-loop tracking control operations in case of unreliable measurements. On the plant side, a safety verification module and a local emergency controller are designed to manage severe attack scenarios that cannot be handled on the controller's side. These two modules resort to worst-case reachability and controllability data-driven arguments to detect potential unsafe scenarios and replace, whenever strictly needed, the tracking controller with emergency actions whose objective is to steer the plant's state trajectory in a predefined set of admissible and safe robust control invariant region until an attack-free scenario is restored. The effectiveness of the proposed solution has been shown through a simulation example.

eess.SY↗

arXiv2024

A Modular Safety Filter for Safety-Certified Cyber-Physical Systems

Nowadays, many control systems are networked and embed communication and computation capabilities. Such control architectures are prone to cyber attacks on the cyberinfrastructure. Consequently, there is an impellent need to develop solutions to preserve the plant's safety against potential attacks. To ensure safety, this paper introduces a modular safety filter approach that is effective for various cyber-attack types. This solution can be implemented in combination with existing control and detection algorithms, effectively separating safety from performance. The safety filter does not require information on the received command's reliability or the anomaly detector's feature. It can be implemented in conjunction with high-performance, resilient controllers to achieve both high performance during normal operation and safety during an attack. As an illustrative example, we have shown the effectiveness of the proposed design considering a multi-agent formation task involving 20 mobile robots. The simulation results testify that the safety filter operates effectively during undetectable, intelligent attacks.

eess.SY↗

arXiv2023

A Data-Driven Safety Preserving Control Architecture for Constrained Cyber-Physical Systems

In this paper, we propose a data-driven networked control architecture for unknown and constrained cyber-physical systems capable of detecting networked false-data-injection attacks and ensuring plant's safety. In particular, on the controller's side, we design a novel robust anomaly detector that can discover the presence of network attacks using a data-driven outer approximation of the expected robust one-step reachable set. On the other hand, on the plant's side, we design a data-driven safety verification module, which resorts to worst-case arguments to determine if the received control input is safe for the plant's evolution. Whenever necessary, the same module is in charge of replacing the networked controller with a local data-driven set-theoretic model predictive controller, whose objective is to keep the plant's trajectory in a pre-established safe configuration until an attack-free condition is recovered. Numerical simulations involving a two-tank water system illustrate the features and capabilities of the proposed control architecture.

eess.SY↗

arXiv2023

Data-Driven Robust Backward Reachable Sets for Set-Theoretic Model Predictive Control

In this paper, we propose a novel approach for computing robust backward reachable sets from noisy data for unknown constrained linear systems subject to bounded disturbances. In particular, we develop an algorithm for obtaining zonotopic inner approximations that can be used for control purposes. It is shown that such sets, if built on an extended space including states and inputs, can be used to embed the system's one-step evolution in the computed extended regions. Such a result is then exploited to build a set-theoretic model predictive controller that, offline, builds a recursive family of robust data-driven reachable sets, and online, computes recursively admissible control actions without explicitly resorting to either a model of the system or the available data.The validity of the proposed data-driven solution is verified by means of a numerical simulation and its performance is contrasted with the model-based counterpart.

eess.SY↗

arXiv2019

Reinforcement Learning for Learning of Dynamical Systems in Uncertain Environment: a Tutorial

In this paper, a review of model-free reinforcement learning for learning of dynamical systems in uncertain environments has discussed. For this purpose, the Markov Decision Process (MDP) will be reviewed. Furthermore, some learning algorithms such as Temporal Difference (TD) learning, Q-Learning, and Approximate Q-learning as model-free algorithms which constitute the main part of this article have been investigated, and benefits and drawbacks of each algorithm will be discussed. The discussed concepts in each section are explaining with details and examples.

cs.LG↗