Modern Industrial Control Systems Research Articles

Automatic control strategy of electronic and electrical engineering based on FPGA

Field Programmable Gate Array (FPGA) have demonstrated significant potential in the field of automation control due to their flexibility, programmability, and high performance. This study designs an FPGA-based automation control strategy for motor control systems, encompassing four core modules: signal acquisition, signal processing, control algorithm, and output drive. The signal acquisition module employs the AD9280 high-speed, high-precision ADC chip to achieve rapid and accurate signal acquisition. The signal processing module utilizes digital filtering and FFT analysis within the FPGA to extract key information such as motor speed. The control algorithm module adopts an adaptive fuzzy control strategy that fine-tunes control rules in real-time to ensure precise control. The output drive module generates PWM signals via the FPGA to drive the motor, guaranteeing efficient and stable operation. Experimental results indicate that the FPGA-based control strategy significantly outperforms traditional methods in terms of response speed and stability, capable of meeting the high-performance requirements of modern industrial motor control systems. This research provides new technical insights and practical references for the field of automation control in electrical and electronic engineering.

Influence of Discretization on the Speed Gradient Synchronization Control

Increasing the productivity of technological operations is a current task of modern science. The introduction of modern industrial equipment control systems is associated with the digitalization and computerization of enterprises. Vibration technology is one of the common types of industrial equipment used for screening, crushing, vibratory movement, etc. The energy approach for the vibration setups control makes it possible to keep a constant level of total energy of vibration setup oscillations, which makes it possible to develop intelligent control system under conditions of uncertainty in the parameter space. This paper is devoted to the study of the influence of digitalization and discretization on the speed gradient algorithm operation for the multiple synchronization control of vibration setup rotors and evaluation of the critical sensor signals sampling steps. The paper presents the results of numerical simulation based on the system dynamics equations and approximate values of vibration setup parameters. The simulation results present that an increase of the discretization sampling step leads to a disruption of the multiple synchronization mode up to the stability loss. The results of an experimental study on a mechatronic vibration setup SV-2M demonstrate in normal operation mode the low-frequency oscillations of the rotors speeds and the total system energy, which frequency is determined by the control signal limit. When the discretization step increase, the motion with stops is observed, which has a similar nature with the stable relaxation self-oscillations. The practical relevance of the obtained results is a detection of possible effects that occur in the system with significant discretization steps. Further development of adaptive control systems can be aimed to compensating of the discretization effect on the operation of the speed gradient control of the vibration setup rotors synchronization.

Tracking Control of Multi-Agent Systems Using a Networked Predictive PID Tracking Scheme

With the rapid development of network technology and control technology, a networked multi-agent control system is a key direction of modern industrial control systems, such as industrial Internet systems. This paper studies the tracking control problem of networked multi-agent systems with communication constraints, where each agent has no information on the dynamics of other agents except their outputs. A networked predictive proportional integral derivative (PPID) tracking scheme is proposed to achieve the desired tracking performance, compensate actively for communication delays, and simplify implementation in a distributed manner. This scheme combines the past, present and predictive information of neighbour agents to form a tracking error signal for each agent, and applies the proportional, integral, and derivative of the agent tracking error signal to control each individual agent. The criteria of the stability and output tracking consensus of multi-agent systems with the networked PPID tracking scheme are derived through detailed analysis on the closed-loop systems. The effectiveness of the networked PPID tracking scheme is illustrated via an example.

Blockchain-based model for tracking compliance with security requirements

The increasing threat landscape in Industrial Control Systems (ICS) brings different risk profiles with comprehensive impacts on society and safety. The complexity of cybersecurity risk assessment increases with a variety of third-party software components that comprise a modern ICS supply chain. A central issue in software supply chain security is the evaluation whether the secure development lifecycle process (SDL) is being methodologically and continuously practiced by all vendors. In this paper, we investigate the possibility of using a decentralized, tamper-proof system that will provide trustworthy visibility of the SDL metrics over a certain period, to any authorized auditing party. Results of the research provide a model for creating a blockchain-based approach that allows inclusion of auditors through a consortium decision while responding to SDL use cases defined by this paper. The resulting blockchain architecture successfully responded to requirements mandated by the security management practice as defined by IEC 62443-4-1 standard.

Research on Identification Algorithm of Cascade Control System

Aiming at the problem of object model identification of modern industrial process control systems, a new closed-loop moment parameter identification online method based on the data of normal operation of the running system is proposed. In this method, only one step response data of the system is required, and appropriate convergence factors are introduced into the Laplace formula, the trapezoidal integral method is used to calculate the values of two derivatives of the transfer function, then the four unknown parameters of the second-order model can be solved by fitting the data with the least square method, and the target model can be identified. Finally, the simulation results of building different objects through Matlab show that the identification method has general applicability and good robustness with high recognition, and it is not sensitive to noise signals.

INVESTIGATION OF CLUSTERING AND CLASSIFICATION METHODS FOR INTELLECTUAL ANALYSIS OF LOG FILES

Today, the application of information technology in all areas of our lives has led to wider spread and popularity of cybercrime. In modern industrial control systems and cyber-physical systems, log files are very important in terms of detecting cyber incidents, identifying and preventing threats and anomalies. However, today, a large volume of log files generated in these systems greatly complicates the process of extracting useful information from them. This, in turn, highlights the need for intellectual analysis of log files. To this end, this article explores a number of clustering and classification methods and algorithms for the intellectual analysis of log files. Thus, K-means, CURE, EM, kNN, Naive Bayes and DT algorithms are selected out of these algorithms and their working principle is studied, explained, and the application of each algorithm on KDD CUP 99 data set is studied and compared.

Detection and Blocking Method against DLL Injection Attack Using PEB-LDR of ICS EWS in Smart IoT Environments

<p>Modern Industrial Control System (ICS) can provide vast functions as the introduction of IT technology is established along with the introduction of the IoT environment. Engineering Workstation (EWS) used by ICS is widely used to efficiently manage and control industrial devices including smart IoT devices. However, the DLL injection attack in ICS is not high in difficulty compared to the risk, but it can cause fatal malfunction. If an attack is carried out targeting the EWS, it may cause erroneous operation in many control devices, including IoT devices, cause fatal accidents throughout the Supervisory Control and Data Acquisition (SCADA) system. In this paper, we present a method to detect DLL injection attacks by specializing in EWS used in ICS in IoT environment and purpose a method to detect data changes due to DLL injection attacks by analyzing and utilizing PEB-LDR data. Also, we purpose a method to detect and block execution when a malicious DLL is suspected to be loaded by DLL injection.</p> <p> </p>

Simulation Approach for Day-Ahead Production Scheduling of a Power Plant.

This paper targets to show a practical realization of such contemporary notions of modern industrial IT as ‘digitalization’ and ‘big data’ in referent example of energy producing enterprise. Huge historical databases of technological parameters accumulated by modern industrial control systems has transformed to a precious asset of modern companies. Deep knowledge extraction approach applied to the accumulated data can bring additional benefits. The research grounds on classical methodology of mathematical simulation. Using a programmatic realization of numerical Monte-Carlo procedure we create empirical simulation models of real energy producing assets based on their observed behavior. As a result we are able to conduct so called ‘what-if’ analysis and compare different production scenarios for modeled assets. The investigation approach includes the following steps: approximate the output power characteristics of the power generating units; calculate cross-correlation for all output powers with all available measurements collected by power station SCADA system; create several day working scenarios for research; for each possible approximation and all combinations of influencing parameters conduct Monte-Carlo recalculation sessions to define the scenario that gives better day margin for power plant.

Zero-Trust Principles for Legacy Components

In this paper we briefly outline as set of rules for integration of legacy devices into a modern industrial control system. These rules are fairly simple, and are mostly derived from “Zero Trust” principles. These rules aim to be pragmatic, and cost-effectiveness trumps completeness.

Response Time Analysis for Nonperiodic CAN Message Based on GI/G/1 Queue Model

Controller area network (CAN) has wide applications in modern industrial control systems. Automobile manufacturers use this communication protocol for vehicle control, which is subject to real-time and high-reliability performance. However, with the increasing actuators gathered in the vehicle, time delay will lead to a serious problem for the vehicle safety and performance control. Thus, the information transmission stability of the CAN message needs to be investigated. In order to find out the delay response time of nonperiodic CAN message in the vehicle bus area, the response of the message transmitted in the CAN bus is modeled based on the GI/G/1 queue theory. The test platform is developed to verify the methodology. In the experimental test, some conditions with different IDs, load ratios, and priorities are investigated. Experimental results are compared with the theoretical results, and the simulation results show that the method is valid and verify the real response delay time of nonperiodic CAN message.

Soft Computing Techniques and Their Applications in Intel-ligent Industrial Control Systems: A Survey

Soft computing involves a series of methods that are compatible with imprecise information and complex human cognition. In the face of industrial control problems, soft computing techniques show strong intelligence, robustness and cost-effectiveness. This study dedicates to providing a survey on soft computing techniques and their applications in industrial control systems. The methodologies of soft computing are mainly classified in terms of fuzzy logic, neural computing, and genetic algorithms. The challenges surrounding modern industrial control systems are summarized based on the difficulties in information acquisition, the difficulties in modeling control rules, the difficulties in control system optimization, and the requirements for robustness. Then, this study reviews soft-computing-related achievements that have been developed to tackle these challenges. Afterwards, we present a retrospect of practical industrial control applications in the fields including transportation, intelligent machines, process industry as well as energy engineering. Finally, future research directions are discussed from different perspectives. This study demonstrates that soft computing methods can endow industry control processes with many merits, thus having great application potential. It is hoped that this survey can serve as a reference and provide convenience for scholars and practitioners in the fields of industrial control and computer science.

The Design and Research of Electric Automation Control System Based on PLC

In recent years, programmable logic controller (PLC), a new technology for electrical control, has developed rapidly and has been widely used in various production processes and automatic control of production machinery. It has the characteristics of convenient operation, simple and easy to understand, and high reliability, has become one of the most popular, most important, and most applied industrial control devices. It brings new vitality to power automation control technology, improves the efficiency of power work, and becomes one of the important pillars in the field of modern industrial automation control systems. This article aims to study the design of PLC-based electrical automation control system. Based on the analysis of the classification of PLC, the basic structure of PLC and the working principle of PLC, the PLC power automation control system is designed, and the system is simulated and tested. The test results show that the operation effect of the traditional electrical control system can not meet all the automatic working conditions, and the overall performance is poor, while the system in this paper performs well, indicating that the design of this paper is effective.

Improving Operational Efficiency Through Alarm Management in Water Treatment Processes Using Artificial Intelligence

Water Treatment Plants are controlled by modern industrial process control systems like SCADA or DCS. This facilitates to monitor, control, and troubleshoot water treatment processes and helps in maintaining continuous supply of water with adequate quality. At times and in contrary, these systems hamper process control by generating far too many alarms than needed. Many of the alarms are nuisance in nature and do not indicate any real abnormality. The true alarms which require prompt operator actions to normalize the process are often buried in the pool of nuisance alarms causing significant challenge for operator to take appropriate corrective actions in a timely manner. Many of the past major incidents occurring in the major process industries were attributed to operators’ inability to identify true alarms and take necessary actions. In this paper, we propose an Artificial Intelligence (AI) based pattern mining and advisory system to improve operational efficiency in alarm management. The identified alarm patterns bring out actionable insights in data by (i) identifying nuisance, chattering, redundant alarms, and (ii) Alarm response Pattern. A novel technique for sequential pattern mining in industrial Alarm & Event log data was developed based on State-of-the-art AI based association rule and pattern mining. The efficacy of the proposed method for systematically improving alarm management system in an actual plant environment is currently being studied in a water treatment plant in Singapore.

A Unified Architectural Approach for Cyberattack-Resilient Industrial Control Systems

With the rapid development of functional requirements in the emerging Industry 4.0 era, modern industrial control systems (ICSs) are no longer isolated islands, making them more vulnerable to various cyberattack threats. Cyberattacks on ICSs may have disruptive consequences, such as significant social and economic losses. To proactively address the security issue of ICSs, this article presents a unified architectural approach from the perspectives of cyberthreats on ICSs, security-related ICS technologies, and methods for ICSs. It incorporates secure networks, secure control systems, secure physical processes, and their interactions seamlessly into a unified framework. To increase the resistance of ICSs against intrusions, the network security in our architectural approach is to secure the data in motion through the integration of secure network architecture, secure industrial network protocols, and secure end-to-end communications. The protection of control systems in our architectural approach is risk-based and hierarchical and encompasses prevention- and tolerance-centric defenses. It provides a layer-by-layer defense so that an acceptable level of cybersecurity risk is achieved and maintained. Aiming to maintain the stable operation of physical ICS processes, the secure control in our architectural approach implements a security process against process-aware attacks through a resilient safety control scheme. The global and systematic architectural approach presented in this article for the ICS cybersecurity will help facilitate the design and implementation of cyberattack-resilient ICSs in the networked world. For further development of ICS security technologies, emerging challenges are identified and discussed to motivate future research efforts.

A Model-Driven Alarms Framework (MAF) With Mobile Clients Support for Wide-Ranging Industrial Control Systems

An efficient and robust alarms management is an extremely desirable feature to prevent critical failures in modern Industrial Control Systems. Generally, alarm systems are categorized into two major components; i.e., Alarm Server and Alarm Client . Alarm server is responsible to process alarms on real time values, while alarm clients display the generated alarms. With the advent of modern technologies, like, Internet of Thing (IoT), the need for mobile alarm clients has significantly been increased, as alarms need to be displayed on different gadgets instead of traditional big screens. Owing to significance of alarms in the industry, several state-of-the-art approaches have been introduced, suggesting various improvements. However, such approaches are only meant for specific industry and cannot be applied across-the-board. Furthermore, full support for mobile clients is not available. On the other hand, certain industrial software products like, SIMATIC WinCC, Genesis64 provide complete alarm solution including mobile client features , as well. However, such solutions are proprietary with higher costs, i.e., pay per tag model. Considering such limitations, this article proposes a novel M odel-driven A larms F ramework ( MAF ) with mobile clients ( Android and iOS ) support. Particularly, MAF comprises an A larm P rofile for I ndustrial C ontrol S ystems ( APICS ) for the modeling of server , mobile clients and configuration requirements of alarms with remarkable simplicity. Furthermore, a complete open source Alarms Profile Transformation Engine ( APTE ) has been implemented to automatically generate alarm server (Kotlin), native Android (Kotlin) and iOS (Swift) alarm clients from APICS-compliant models. The feasibility of proposed framework is demonstrated through two case studies ( flour mill and home automation ). The results prove that MAF not only provides complete alarm server solution to generate bulk of alarms simultaneously, but also supports android and iOS alarm clients for efficient visualization. Besides that, MAF is easy to use , cost-effective and can be applied in wide-ranging industrial applications.

A Two Stage Intrusion Detection System for Industrial Control Networks Based on Ethernet/IP

Standard Ethernet (IEEE 802.3 and the TCP/IP protocol suite) is gradually applied in industrial control system (ICS) with the development of information technology. It breaks the natural isolation of ICS, but contains no security mechanisms. An improved intrusion detection system (IDS), which is strongly correlated to specific industrial scenarios, is necessary for modern ICS. On one hand, this paper outlines three kinds of attack models, including infiltration attacks, creative forging attacks, and false data injection attacks. On the other hand, a two stage IDS is proposed, which contains a traffic prediction model and an anomaly detection model. The traffic prediction model, which is based on the autoregressive integrated moving average (ARIMA), can forecast the traffic of the ICS network in the short term and detect infiltration attacks precisely according to the abnormal changes in traffic patterns. Furthermore, the anomaly detection model, using a one class support vector machine (OCSVM), is able to detect malicious control instructions by analyzing the key field in Ethernet/IP packets. The confusion matrix is selected to testify to the effectiveness of the proposed method, and two other innovative IDSs are used for comparison. The experiment results show that the proposed two stage IDS in this paper has an outstanding performance in detecting infiltration attacks, forging attacks, and false data injection attacks compared with other IDSs.

Cyber attack detection and mitigation: Software Defined Survivable Industrial Control Systems

Modern Industrial Control Systems (ICS) constitute complex and heterogeneous ‘system of systems’ embracing the numerous advantages of traditional Information and Communication Technology (ICT). The pervasive integration of off-the-shelf ICT into the core of ICS broadened the palette of features and applications, but it also raised new design challenges and exposed ICS to a new breed of cyber-physical attacks. In addition, despite all the security solutions in place, unavoidably, these systems may be compromised. Therefore, survivability, that is, the ability to face malicious actions and faults, becomes a salient feature/requirement in the design of modern cyber-connected ICS. We present a comprehensive solution for ensuring the survival of ICS under malicious activities and faults. We design a Software Defined Networking (SDN) and Network Function Virtualization (NFV) based communication infrastructure particularly tailored to address the communication requirements of ICS. We develop an attack detection and localization algorithm for bidirectional ICS flows, and we design an optimal intervention strategy that embraces the communication and security requirements of industrial applications. Finally, we present intrinsic details on recreating a real-life and emulated test infrastructure. Experimental results demonstrate the solution’s applicability to networked robot control systems.

Detecting stealthy attacks against industrial control systems based on residual skewness analysis

With the integration of the modern industrial control systems (ICS) with the Internet technology, ICS can make full use of the rich resources on the Internet to facilitate remote process control. However, every coin has two sides. More exposure to the outside IT world has made ICS an attractive target for hackers, so it becomes urgent to protect the security of ICS. Skilled attackers can penetrate control networks and then manipulate sensor readings or control signals persistently until the system crashes, while still keeping themselves undetected by following the expected behavior of the system closely. This kind of attacks are referred to as stealthy attacks. As far as we know, many existing intrusion detection techniques only investigate the magnitudes of behavior residuals, so they cannot detect this kind of stealthy attacks. In this paper, we discover that residuals generated during stealthy attacks exhibit significant skewness compared to attack-free residuals. Based on the new observation, we propose an effective and fast technique to detect stealthy attacks against ICS based on residual skewness analysis. Skewness coefficients can distinguish the counterfeited residuals from the attack-free residuals effectively. A larger absolute value of the skewness coefficient generally indicates the occurrence of a more intense stealthy attack. Finally, we conduct comprehensive experiments to verify the effectiveness and efficiency of the proposed stealthy attack detection approach.

A review of security assessment methodologies in industrial control systems

PurposeThe common implementation practices of modern industrial control systems (ICS) has left a window wide open to various security vulnerabilities. As the cyber-threat landscape continues to evolve, the ICS and their underlying architecture must be protected to withstand cyber-attacks. This study aims to review several ICS security assessment methodologies to identify an appropriate vulnerability assessment method for the ICS systems that examine both critical physical and cyber systems so as to protect the national critical infrastructure.Design/methodology/approachThis paper reviews several ICS security assessment methodologies and explores whether the existing methodologies are indeed sufficient to meet the cyber security assessment exercise required to validate the security of electrical power control systems.FindingsThe study showed that most of the examined methodologies seem to concentrate on vulnerability identification and prioritisation techniques, whilst other security techniques received noticeably less attention. The study also showed that the least attention is devoted to patch management process due to the critical nature of the SCADA system. Additionally, this review portrayed that only two security assessment methodologies exhibited absolute fulfilment of all NERC-CIP security requirements, whilst the others only partially fulfilled the essential requirements.Originality/valueThis paper presents a review and a comparative analysis of several standard SCADA security assessment methodologies and guidelines published by internationally recognised bodies. In addition, it explores the adequacy of the existing methodologies in meeting cyber security assessment practices required for electrical power networks.

An Enhanced Multi-Stage Semantic Attack Against Industrial Control Systems

Industrial Control Systems (ICS) play a very important role in national critical infrastructures. However, the growing interaction between the modern ICS and the Internet has made ICS more vulnerable to cyber attacks. In order to protect ICS from malicious attacks, intrusion detection technology emerges. By analyzing the network meta data or the industrial process data, Intrusion Detection Systems (IDS) can identify attacks that violate communication protocols or system specifications. However, the existing intrusion detection technology is not omnipotent, which opens up a back door for some more advanced attacks. In this work, we design an enhanced multi-stage semantic attack against ICS, which is undetectable by existing IDS. By hijacking the communication channels between the Human Machine Interface (HMI) and the remote Programmable Logic Controllers (PLCs), the attacker can manipulate the measurement data and control instructions simultaneously. The fake measurement data deceives the human operator into making wrong decisions. Furthermore, the attacker can strategically manipulate the semantic meaning of control instructions according to system state transition rules. In the meanwhile, a fake view of measurement data is presented to the HMI to conceal the on-going malicious attack. This attack is totally stealthy since the message sizes and timing, the command sequences, and the system state values are all legitimate. Consequently, this attack can secretly bring the system into critical states. Experimental results have verified the strong attack ability of the proposed attack.