Cyber-physical system

About: Cyber-physical system is a research topic. Over the lifetime, 11096 publications have been published within this topic receiving 162489 citations. The topic is also known as: CPS.

Guest Editorial Special Issue on Secure Control of Cyber-Physical Systems

Abstract: The papers in this special section focus on inititives to security cyber-physical systems. The integration of computation, communication, and control units has led to the birth and rapid development of a new generation of engineering systems, the cyber-physical systems (CPSs), which have been increasingly used in fields ranging from aerospace, automobile, industrial process control to energy, healthcare, manufacturing, and transportation, where secure operation is one of the key concerns. These CPSs are often distributed in nature with a hierarchical structure, and likely to be attacked due to diverse motivations, such as economic reasons (e.g., reducing or not paying electricity charge), or terrorism (e.g., threatening people by controlling electricity and other life-critical resources).

Automatic Parameter Estimation for Reusable Software Components of Modular and Reconfigurable Cyber-Physical Production Systems in the Domain of Discrete Manufacturing

Abstract: The main feature of cyber-physical production systems is its adaptability. They adapt quickly to new requirements such as new products or product variants. Nowadays, a bottleneck is the automation system, for which high manual engineering efforts are needed: Today, on-site technicians write and rewrite automation software, configure real-time communication protocols and create system configurations consisting of machine timing, physical dimensions of products, sensitivity, and motor control accelerations and velocities. Cyber-physical production systems often solve this dilemma by relying on reusable software components, which are composed in the overall automation software. However, this solution comes with a price, reusable software components need free parameters to adjust to the individual production configurations. This paper addresses this central research question and presents a novel parameter estimation approach to choose automatically optimal system configurations for cyber-physical production systems. Different scenarios from discrete manufacturing plants are used to evaluate the solution approach.