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Control Part of Cyber-Physical Systems: Modeling, Design and Analysis

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A special issue of Energies (ISSN 1996-1073). This special issue belongs to the section "A1: Smart Grids and Microgrids".

Deadline for manuscript submissions: closed (24 February 2023) | Viewed by 15779

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Special Issue Editors

Prof. Dr. Remigiusz Wiśniewski

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Guest Editor

Institute of Control and Computation Engineering, University of Zielona Góra, Prof. Z. Szafrana 2, 65-516 Zielona Góra, Poland
Interests: Petri nets; FPGA; cyber–physical systems; concurrent systems; design, analysis, and modeling of CPS; cybersecurity
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Prof. Dr. Shaohua Wan

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Guest Editor

School of Information and Safety Engineering, Zhongnan University of Economics and Law, Wuhan 430073, China
Interests: wireless networks; internet of things; cyber physical systems; edge intelligence; smart grid; internet of vehicles
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

The cyber-physical system (CPS) can be seen as an integration of computation with physical processes, defined by cyber and physical parts of the system. The cyber part controls the objects and makes decisions, while the physical part refers to the real world and is prone to environmental influences. This Special Issue concerns the modeling, design, and analysis aspects of the control part of a cyber-physical system. Topics of interest include but are not limited to:

Modeling of the control part of CPS:
- Concurrent models (including Petri nets, UML diagrams, and others);
- Sequential models (including FSMs, compositional automata, and others);
- Hierarchical state machines and Statecharts;
- Deterministic models of the control part of CPS;
- Other modeling aspects of the control part of CPS.
Design of the control part of CPS:
- Integrated and distributed implementation of the control part of CPS;
- Hardware realization (including FPGAs, embedded processors, PLCs, etc.);
- Decomposition (splitting) and synchronization techniques of the control part of CPS;
- Static and dynamic partial reconfiguration of the control part of CPS.
Analysis of the control part of CPS:
- Verification of the control part (including formal methods, model checking, etc.);
- Validation of the control part (model simulation, software and hardware simulation);
- Analysis of the concurrency and sequentiality relations in the control part of CPS;
- Security of the control part of CPS (including algorithms, protocols, e-services, etc.).

Prof. Dr. Remigiusz Wiśniewski
Prof. Dr. Shaohua Wan
Guest Editors

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Keywords

Cyber-physical system (CPS)
Control part of CPS
Modeling
Design
Analysis
Petri net
Field programmable gate array (FPGA)
Dynamic and static partial reconfiguration of FPGA
Industrial Internet of Things
Edge intelligence
Computation intelligence

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Published Papers (9 papers)

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21 pages, 2665 KiB

Open AccessArticle

Designing Reconfigurable Cyber-Physical Systems Using Unified Modeling Language

by Grzegorz Bazydło

Energies 2023, 16(3), 1273; https://doi.org/10.3390/en16031273 - 25 Jan 2023

Cited by 7 | Viewed by 2706

Abstract

Technological progress in recent years in the Cyber-Physical Systems (CPSs) area has given designers unprecedented possibilities and computational power, but as a consequence, the modeled CPSs are becoming increasingly complex, hierarchical, and concurrent. Therefore, new methods of CPSs design (especially using abstract modeling) are needed. The paper presents an approach to the CPS control part modeling using state machine diagrams from Unified Modelling Language (UML). The proposed design method attempts to combine the advantages of graphical notation (intuitiveness, convenience, readability) with the benefits of text specification languages (unambiguity, precision, versatility). The UML specification is transformed using Model-Driven Development (MDD) techniques into an effective program in Hardware Description Language (HDL), using Concurrent Finite State Machine (CFSM) as a temporary model. The obtained HDL specification can be analyzed, validated, synthesized, and finally implemented in Field Programmable Gate Array (FPGA) devices. The dynamic, partial reconfiguration (a feature of modern FPGAs) allows for the exchange of a part of the implemented CPS algorithm without stopping the device. But to use this feature, the model must be safe, which in the proposed approach means, that it should possess special idle states, where the control is transferred during the reconfiguration process. Applying the CFSM model greatly facilitates this task. The proposed design method offers efficient graphical modeling of a control part of CPS, and automatic translation of the behavior model into a synthesizable Verilog description, which can be directly implemented in FPGA devices, and dynamically reconfigured as needed. A practical example illustrating the successive stages of the proposed method is also presented. Full article

(This article belongs to the Special Issue Control Part of Cyber-Physical Systems: Modeling, Design and Analysis)

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19 pages, 2690 KiB

Open AccessArticle

Design and Verification of Petri-Net-Based Cyber-Physical Systems Oriented toward Implementation in Field-Programmable Gate Arrays—A Case Study Example

by Remigiusz Wiśniewski, Marcin Wojnakowski and Zhiwu Li

Energies 2023, 16(1), 67; https://doi.org/10.3390/en16010067 - 21 Dec 2022

Cited by 15 | Viewed by 2016

Abstract

This paper presents a novel design approach of a Petri-net-based cyber-physical system (CPS). The idea is oriented toward implementation in a field-programmable gate array (FPGA). The proposed technique permits error detection in the system at the early specification stage in order to reduce the time and prototyping cost of the CPS. Due to the state explosion problem, the traditional verification methods have exponential computational complexity. In contrast, we show that under certain assumptions, the proposed algorithm is able to detect possible errors in the system even in cubic O(

{| T |}^{2} | P |

) time. Furthermore, all the required steps of the proposed design method are presented and discussed. The idea is illustrated by a real-life case study example of a traffic light crossroad. The system was modelled, analysed, implemented, and finally validated within the FPGA device (Virtex-5 family). Full article

(This article belongs to the Special Issue Control Part of Cyber-Physical Systems: Modeling, Design and Analysis)

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32 pages, 8442 KiB

Open AccessArticle

Improving Characteristics of LUT-Based Sequential Blocks for Cyber-Physical Systems

by Alexander Barkalov, Larysa Titarenko and Kazimierz Krzywicki

Energies 2022, 15(7), 2636; https://doi.org/10.3390/en15072636 - 4 Apr 2022

Cited by 1 | Viewed by 1644

Abstract

A method is proposed for optimizing circuits of sequential devices which are used in cyber-physical systems (CPSs) implemented using field programmable gate arrays (FPGAs). The optimizing hardware is a very important problem connected with implementing digital parts of CPSs. In this article, we discuss a case when Mealy finite state machines (FSMs) represent behaviour of sequential devices. The proposed method is aimed at optimization of FSM circuits implemented with look-up table (LUT) elements of FPGA chip. The method aims to reduce the LUT count of Mealy FSMs with extended state codes. The method is based on finding a partition of the set of internal states by classes of compatible states. To reduce LUT count, we propose a special kind of state codes named composite state codes. The composite codes include two parts. The first part includes the binary codes of states as elements of some partition class. The second part consists of the code of corresponding partition class. Using composite state codes allows us to obtain FPGA-based FSM circuits with exactly two levels of logic. If some conditions hold, then any FSM function from the first level is implemented by a single LUT. The second level is represented as a network of multiplexers. Each multiplexer generates either an FSM output or input memory function. An example of synthesis is shown. The experiments prove that the proposed approach allows us to reduce hardware compared with two methods from Vivado, JEDI-based FSMs, and extended state assignment. Depending on the complexity of an FSM, the LUT count is reduced on average from 15.46 to 68.59 percent. The advantages of the proposed approach grow with the growth of FSM complexness. An additional positive effect of the proposed method is a decrease in the latency time. Full article

(This article belongs to the Special Issue Control Part of Cyber-Physical Systems: Modeling, Design and Analysis)

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18 pages, 10255 KiB

Open AccessArticle

An FEA-Assisted Decision-Making Framework for PEMFC Gasket Material Selection

by Kang-Min Cheon, Ugochukwu Ejike Akpudo, Akeem Bayo Kareem, Okwuosa Chibuzo Nwabufo, Hyeong-Ryeol Jeon and Jang-Wook Hur

Energies 2022, 15(7), 2580; https://doi.org/10.3390/en15072580 - 1 Apr 2022

Cited by 4 | Viewed by 2545

Abstract

Recent research studies on industrial cyber-physical systems (ICPSs) have witnessed vast patronage with emphasis on data utility for improved design, maintenance, and high-level decision making. The design of proton-exchange membrane fuel cells (PEMFC) is geared towards improving performance and extending life cycles. More often, material selection of PEMFC components contributes a major determining factor for efficiency and durability with the seal/gasket quality being one of the most critical components. Finite element analysis (FEA) offers a simulated alternative to real-life stress analysis of components and has been employed on different rubber-like gasket materials for hydrogen fuel cells for determining an optimal strain energy density function using different hyperelastic models following uniaxial tensile testing. The results show that the Mooney–Rivlin, Ogden, and Yeoh models were the most fitting model with the best stress–strain fit following a weighted error evaluation criteria which returned 18.54%, 19.31%, and 21.96% for 25% displacement, and 22.1%, 21.7%, and 21.17% for 40% displacements, respectively. Further empirical analysis using the multi-metric regression technique for compatibility testing (curve similarity) between the hyperelastic model outputs and the tensile data reveal that the Yeoh model is the most consistent as seen in the marginal error difference amidst increasing displacement while the Arruda–Boyce model is most inconsistent as shown in the high error margin as the displacement increases from 25% to 40%. Lastly, a comparative assessment between different rubber-like materials (RLM) was presented and is expected to contribute to improved decision-making and material selection. Full article

(This article belongs to the Special Issue Control Part of Cyber-Physical Systems: Modeling, Design and Analysis)

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18 pages, 5173 KiB

Open AccessArticle

Investigating the Efficiencies of Fusion Algorithms for Accurate Equipment Monitoring and Prognostics

by Ugochukwu Ejike Akpudo and Jang-Wook Hur

Energies 2022, 15(6), 2204; https://doi.org/10.3390/en15062204 - 17 Mar 2022

Cited by 5 | Viewed by 2033

Abstract

Recent findings suggest the need for optimal condition monitoring due to increasing counter-productive issues ranging from threats to life, malware, and hardware failures. Several prognostic schemes have been reported across many disciplines; however, the issues of sensor data discrepancy emanating from varying loading and operating conditions of cyber-physical system (CPS) components still remain a challenging factor. Nonetheless, a significant part of these prognostic schemes comprises a sensor/feature fusion module for comprehensive health indicator (HI) construction. This study investigates the prowess of unsupervised fusion algorithms for constructing optimal HI construction on two publicly available datasets—a simulated turbofan engine degradation experiment and an actual production plant condition monitoring dataset. The fusion efficiencies of the algorithms were evaluated using standard metrics for prognostic parameter assessments. The results show that the autoencoder is more reliable for real-life applications, including cases with uniform degradation patterns and the more complex scenarios with irregular degradation paths in the sensor measurements/features, and is expected to direct continued research for improved multi-sensor-based prognostics and health management of industrial equipment. Full article

(This article belongs to the Special Issue Control Part of Cyber-Physical Systems: Modeling, Design and Analysis)

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16 pages, 986 KiB

Open AccessArticle

Dynamic Lyapunov Machine Learning Control of Nonlinear Magnetic Levitation System

by Amr Mahmoud and Mohamed Zohdy

Energies 2022, 15(5), 1866; https://doi.org/10.3390/en15051866 - 3 Mar 2022

Cited by 2 | Viewed by 2316

Abstract

This paper presents a novel dynamic deep learning architecture integrated with Lyapunov control to address the timing latency and constraints of deep learning. The dynamic component permits the network depth to increase or decrease depending on the system complexity/nonlinearity evaluated through the parameterized complexity method. A correlation study between the parameter tuning effect on the error is also made thus causing a reduction in the deep learning time requirement and computational cost during the network training and retraining process. The control Lyapunov function is utilized as an input cost function to the DNN in order to determine the system stability. A relearning process is triggered to account for the introduction of disturbances or unknown model dynamics, therefore, eliminating the need for an observer-based approach. The introduction of the relearning process also allows the algorithm to be applicable to a wider array of cyber–physical systems (CPS). The intelligent controller autonomy is evaluated under different circumstances such as high frequency nonlinear reference, reference changes, or disturbance introduction. The dynamic deep learning algorithm is shown to be successful in adapting to such changes and reaching a safe solution to stabilize the system autonomously. Full article

(This article belongs to the Special Issue Control Part of Cyber-Physical Systems: Modeling, Design and Analysis)

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25 pages, 59253 KiB

Open AccessArticle

Design of Petri Net-Based Cyber-Physical Systems Oriented on the Implementation in Field Programmable Gate Arrays

by Remigiusz Wisniewski

Energies 2021, 14(21), 7054; https://doi.org/10.3390/en14217054 - 28 Oct 2021

Cited by 19 | Viewed by 1908

Abstract

Two design flows of the Petri net-based cyber-physical systems oriented towards implementation in an FPGA are presented in the paper. The first method is based on the behavioural description of the system. The control part of the cyber-physical system is specified by an interpreted Petri net, and is described directly in the synthesisable Verilog hardware language for further implementation in the programmable device. The second technique involves splitting the design into sequential modules. In particular, adequate decomposition and synchronisation algorithms are proposed. The resulting modules are further modelled within the Verilog language as the composition of sequential automata. The presented design flows are supported by theoretical background, and templates of Verilog codes. The proposed techniques are illustrated by a real-life example of a multi-robot cyber-physical system, where each step of the proposed flows is explained in detail, including modelling, description of the system in the Verilog language, and final implementation within the FPGA device. The results obtained during the verification and validation confirm the proper functionality of the system designed by both design flows. Full article

(This article belongs to the Special Issue Control Part of Cyber-Physical Systems: Modeling, Design and Analysis)

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13 pages, 674 KiB

Open AccessArticle

D-dCNN: A Novel Hybrid Deep Learning-Based Tool for Vibration-Based Diagnostics

by Ugochukwu Ejike Akpudo and Jang-Wook Hur

Energies 2021, 14(17), 5286; https://doi.org/10.3390/en14175286 - 26 Aug 2021

Cited by 14 | Viewed by 2748

Abstract

This paper develops a novel hybrid feature learner and classifier for vibration-based fault detection and isolation (FDI) of industrial apartments. The trained model extracts high-level discriminative features from vibration signals and predicts equipment state. Against the limitations of traditional machine learning (ML)-based classifiers, the convolutional neural network (CNN) and deep neural network (DNN) are not only superior for real-time applications, but they also come with other benefits including ease-of-use, automated feature learning, and higher predictive accuracies. This study proposes a hybrid DNN and one-dimensional CNN diagnostics model (D-dCNN) which automatically extracts high-level discriminative features from vibration signals for FDI. Via Softmax averaging at the output layer, the model mitigates the limitations of the standalone classifiers. A diagnostic case study demonstrates the efficiency of the model with a significant accuracy of 92% (F1 score) and extensive comparative empirical validations. Full article

(This article belongs to the Special Issue Control Part of Cyber-Physical Systems: Modeling, Design and Analysis)

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Review

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24 pages, 2955 KiB

Open AccessReview

Review of Cybersecurity Analysis in Smart Distribution Systems and Future Directions for Using Unsupervised Learning Methods for Cyber Detection

by Smitha Joyce Pinto, Pierluigi Siano and Mimmo Parente

Energies 2023, 16(4), 1651; https://doi.org/10.3390/en16041651 - 7 Feb 2023

Cited by 17 | Viewed by 4149

Abstract

In a physical microgrid system, equipment failures, manual misbehavior of equipment, and power quality can be affected by intentional cyberattacks, made more dangerous by the widespread use of established communication networks via sensors. This paper comprehensively reviews smart grid challenges on cyber-physical and cyber security systems, standard protocols, communication, and sensor technology. Existing supervised learning-based Machine Learning (ML) methods for identifying cyberattacks in smart grids mostly rely on instances of both normal and attack events for training. Additionally, for supervised learning to be effective, the training dataset must contain representative examples of various attack situations having different patterns, which is challenging. Therefore, we reviewed a novel Data Mining (DM) approach based on unsupervised rules for identifying False Data Injection Cyber Attacks (FDIA) in smart grids using Phasor Measurement Unit (PMU) data. The unsupervised algorithm is excellent for discovering unidentified assault events since it only uses examples of typical events to train the detection models. The datasets used in our study, which looked at some well-known unsupervised detection methods, helped us assess the performances of different methods. The performance comparison with popular unsupervised algorithms is better at finding attack events if compared with supervised and Deep Learning (DL) algorithms. Full article

(This article belongs to the Special Issue Control Part of Cyber-Physical Systems: Modeling, Design and Analysis)

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