Fuzzy Control in Cyber-Physical Systems

2020 ◽  
Vol 2 (2) ◽  
pp. 46-58
Author(s):  
Michael Gr. Voskoglou
Keyword(s):  
Fuzzy Control ◽  
Autonomous Vehicles ◽  
Input Data ◽  
Fuzzy Numbers ◽  
Geometric Approach ◽  
Cyber Physical Systems ◽  
Control Model ◽  
Triangular Fuzzy Numbers ◽  
Physical Systems ◽  
Braking System

Controllers are devices regulating the operation of other devices or systems. Fuzzy controllers analyze the input data in terms of variables which take on continuous values in the interval [0, 1]. Since fuzzy logic has the advantage of expressing the solution of the problems in the natural language, the use of fuzzy instead of traditional controllers makes easier the mechanization of tasks that have been already successfully performed by humans. In the present paper a theoretical fuzzy control model is developed for the braking system of autonomous vehicles, which are included among the most characteristic examples of Cyber-Physical Systems. For this, a simple geometric approach is followed using triangular fuzzy numbers as the basic tools.

scholarly journals Design of IoT-based Cyber–Physical Systems: A Driverless Bulldozer Prototype

Information ◽  
2019 ◽  
Vol 10 (11) ◽  
pp. 343 ◽  
Author(s):  
Nelson H. Carreras Guzman ◽  
Adam Gergo Mezovari
Keyword(s):  
Autonomous Vehicles ◽  
Industrial Revolution ◽  
Physical World ◽  
Cyber Physical Systems ◽  
Transportation Systems ◽  
Small Scale ◽  
Security Risk ◽  
Physical Systems ◽  
Wireless Communication Network ◽  
Enhance Efficiency

From autonomous vehicles to robotics and machinery, organizations are developing autonomous transportation systems in various domains. Strategic incentives point towards a fourth industrial revolution of cyber–physical systems with higher levels of automation and connectivity throughout the Internet of Things (IoT) that interact with the physical world. In the construction and mining sectors, these developments are still at their infancy, and practitioners are interested in autonomous solutions to enhance efficiency and reliability. This paper illustrates the enhanced design of a driverless bulldozer prototype using IoT-based solutions for the remote control and navigation tracking of the mobile machinery. We illustrate the integration of a cloud application, communication protocols and a wireless communication network to control a small-scale bulldozer from a remote workstation. Furthermore, we explain a new tracking functionality of work completion using maps and georeferenced indicators available via a user interface. Finally, we provide a preliminary safety and security risk assessment of the system prototype and propose guidance for application in real-scale machinery.

scholarly journals Cyber-physical systems matrix control model

2021 ◽  
Vol 2094 (4) ◽  
pp. 042067
Author(s):  
A V Gurjanov ◽  
D A Zakoldaev ◽  
I O Zharinov ◽  
O O Zharinov
Keyword(s):  
Production Control ◽  
Cyber Physical Systems ◽  
Control Model ◽  
Information Component ◽  
Control Task ◽  
Physical Systems ◽  
Technical Architecture ◽  
Production Complex ◽  
Matrix Control ◽  
Working Out

Abstract The Industry 4.0 technologies oriented for the modern industry as an application to solve the cyber-physical production control general task are viewed. A task to control is positioned as a hierarchy, which require some special schemes cyber-physical systems interaction organization to be developed. The control task hierarchy is converted to the control means hierarchy, within which they preserve the cyber-physical systems groups coordination unity organized in the company functional divisions in structure principle with variable equipment consistency. Information and functional cyber-physical systems interconnection are proposed to be defined within the technical architecture providing cyber-physical production complex automatizing. In the control system they underline the information component realizing not only calculation functions measuring but also net communication. Controlling and being controlled cyber-physical systems are proposed to be united into structures actively interacting with functional company divisions into closed automatic loops working out information and signal actions. There is a cyber-physical production hierarchy structure example given based on control processes tides formalized in physical and virtual levels. There is a cyber-physical systems matrix control model given to coordinate calculations, communications and industrial automatics functionality.

Applications of Fuzzy Numbers to Hyperconnectivity and Computing

2020 ◽  
Vol 4 (2) ◽  
pp. 80-101
Author(s):  
Michael Voskoglou
Keyword(s):  
Industrial Revolution ◽  
Fuzzy Numbers ◽  
Real Life ◽  
Individual Performance ◽  
Cyber Physical Systems ◽  
Future Research ◽  
Physical Systems ◽  
Linear Programming Problems ◽  
The Subject ◽  
A New Technique

The present article focuses on two directions. First, a new fuzzy method using TFNs or TpFNs as tools is developed for evaluating a group's mean performance, when qualitative grades instead of numerical scores are used for assessing its members' individual performance. Second, a new technique is applied for solving linear programming problems with fuzzy coefficients. Examples are presented on real life situations connected to hyper connectivity and computing problems. Such examples illustrate the applicability of our methods in the modern practice of the forthcoming era of a new industrial revolution that will be characterized by the development of an advanced Internet of Things and energy and by the cyber-physical systems controlled through it. A discussion follows for the perspectives of future research on the subject and the article closes with the general conclusions.

scholarly journals Expertise and Uncertainty Processing with Nonlinear Scaling and Fuzzy Systems for Automation

2020 ◽  
Vol 10 (1) ◽  
pp. 712-720
Author(s):  
Esko K. Juuso
Keyword(s):  
Fuzzy Systems ◽  
Data Analytics ◽  
Fuzzy Numbers ◽  
Fuzzy Rule ◽  
Cyber Physical Systems ◽  
Membership Functions ◽  
Physical Systems ◽  
Set Systems ◽  
Recursive Analysis ◽  
Domain Expertise

AbstractIntegration of domain expertise and uncertainty processing is increasingly important in automation solutions which rely on data analytics and artificial intelligence. We need a level to assess what is approximately correct. Uncertainties of the inputs are taken into account by using fuzzy numbers as the inputs of different fuzzy and parametric systems. Nonlinear scaling functions (NSFs) integrate these solutions and make them easier to tune. Fuzzy rule-based systems are represented with scaled fuzzy inputs. Membership functions (MFs) can be developed from NSFs and existing MFs can be used in developing NSFs. Fuzzy set systems and linguistic equation (LE) systems become consistent within the limits of detail. In recursive analysis, both meanings and interactions on all levels can be tuned together with genetic algorithms. In applications, the modular overall system consists of similar subsystems, which are normally used, with extensions to fuzzy. The compact fuzzy modules can be developed for specific tasks which are combined within Cyber Physical Systems (CPS). Uncertainty processing is embedded in the recursive analysis. The fuzzy extensions provide a feasible way for the sensitivity analysis of the solution.

scholarly journals A Survey of Algorithms for Black-Box Safety Validation of Cyber-Physical Systems

2021 ◽  
Vol 72 ◽  
Author(s):  
Anthony Corso ◽  
Robert Moss ◽  
Mark Koren ◽  
Ritchie Lee ◽  
Mykel Kochenderfer
Keyword(s):  
Autonomous Vehicles ◽  
Cyber Physical Systems ◽  
Black Box ◽  
State Spaces ◽  
Physical Systems ◽  
Safety Critical ◽  
Simulated Environment ◽  
Rigorous Testing ◽  
Simulation Based ◽  
Sampling Problem

Autonomous cyber-physical systems (CPS) can improve safety and efficiency for safety-critical applications, but require rigorous testing before deployment. The complexity of these systems often precludes the use of formal verification and real-world testing can be too dangerous during development. Therefore, simulation-based techniques have been developed that treat the system under test as a black box operating in a simulated environment. Safety validation tasks include finding disturbances in the environment that cause the system to fail (falsification), finding the most-likely failure, and estimating the probability that the system fails. Motivated by the prevalence of safety-critical artificial intelligence, this work provides a survey of state-of-the-art safety validation techniques for CPS with a focus on applied algorithms and their modifications for the safety validation problem. We present and discuss algorithms in the domains of optimization, path planning, reinforcement learning, and importance sampling. Problem decomposition techniques are presented to help scale algorithms to large state spaces, which are common for CPS. A brief overview of safety-critical applications is given, including autonomous vehicles and aircraft collision avoidance systems. Finally, we present a survey of existing academic and commercially available safety validation tools.

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