Control Of Industrial Systems Research Articles

Three-model-driven fault diagnosis method for complex hydraulic control system: Subsea blowout preventer system as a case study

Hydraulic control is a control pattern that uses compression fluid as the energy medium and information medium. Hydraulic system is widely used in the control of industrial systems because of its flexibility and reliability. Hydraulic systems have the characteristics of strong fault concealment, significant sensor delay, and a complex signal transmission mechanism. Hence, it is very difficult to identify the faults of systems under the influence of powerful nonlinear time-varying characteristic. The diagnosis of complex hydraulic control system is a problem facing the current research. In order to cope with the challenges caused by limited sensors and concealment of faults, a three-model-driven fault diagnosis method is proposed for complex hydraulic control system. The three hydraulic control system models with regard to energy, fluid and information are proposed and established to explain the work process of the system from different perspectives. The diagnostic model is completed by establishing a fault reasoning model based on the Bayesian network. A redundant control system for subsea blowout preventer is used as a case to demonstrate the proposed method, and the results show that the proposed method has high accuracy.

Intelligent Risk-Based Cybersecurity Protection for Industrial Systems Control—A Feasibility Study

Summary Intelligent automated industrial process control, enabled by IR 4.0 technology, requires increased system integration and connectedness. With such development comes a heightened risk of cyberattack for operational technology (OT) systems, including industrial control systems (ICS) and industrial automation and control systems (IACS), historically shielded from cyberspace. Cyberattacks on OT systems can also impact the physical world. The recent attack on the US Colonial Pipeline interrupted delivery of oil along the US West coast for several days, resulting in a declaration of a state of emergency, while the Ukraine power grid attacks interrupted electrical power supply in Kyiv, impacting thousands of consumers. In 2021, an Equinor-operated platform on the Norwegian Continental shelf was hit by malware. The incident had the potential to affect the drilling control system (DCS). For ICS, the concern is ensuring a safe process, as opposed to the more traditional concern with data protection for information technology (IT) system management. To ensure safe implementation of advanced industrial systems control, while avoiding unnecessary operational downtime, there is a need for cybersecurity solutions that account for risk for the whole cyberphysical system (CPS), including the ICS, the work environment, the product, and the physical surroundings. Diagnostic models and methods must therefore be developed that cover the functionality of the whole CPS. Further, to account for process dynamics and evolving cyberattack threats, adaptability is required with respect to both process state and new types of attacks. For this purpose, knowledge-based awareness monitoring combined with existing cyberattack detection tools for ICS, known as ICS intrusion detection systems (IDS), is proposed, providing an element of artificial intelligence. Results from studies in a DCS environment indicate that existing monitoring applications can be used to detect and discern between different types of cyberattacks on CPSs. This indicates feasibility with respect to monitoring the control and IT components of the ICS system for building risk-based cybersecurity decision support solutions. The key challenge and novelty of the proposed approach is to extend the capability of cybermonitoring in systems control to automated evaluation of process risk, including risk to the physical environment. Such capability will enable appropriate decision support with respect to both process risk and operational downtime.

Identification of twin-shaft gas turbine based on hybrid decoupled state multiple model approach

Monitoring control of industrial systems is essential for the good productivity and safety of installations and operators, with better performance that must be guaranteed. This is often challenging due to the nonlinearities and dynamic complexities of these systems, adding operating constraints and instability. Hence, the multi-models constitute then an adapted tool for the modeling of the nonlinear systems to characterize their dynamic behaviors. Indeed, this work proposes the implementation of a hybrid identification approach of the operating variables of a gas turbine, thus making it possible to interconnect the various linear sub-models with decoupled states in order to generate the global output of their nonlinear model, from the exploitation in real time of the turbine's input/output data. However, the suggested decoupled-state multi-model approach offers an interesting alternative to the optimization procedure of the estimated turbine parameters. By using gradient and Gauss–Newton algorithms, improved by genetic algorithms combined with NSGA-II in hybrid form, in order to converge toward the best solutions with an optimal cost function, the obtained implementation results show that this approach allows the convergence of the estimated turbine variables and describes its behavior in real time, with the guaranteed efficiency of the proposed decoupled state multi-model method.

Design of Multivariate PID Controller for Power Networks Using GEA and PSO

The issue of proper modeling and control for industrial systems is one of the challenging issues in the industry. In addition, in recent years, PID controller design for linear systems has been widely considered. The topic discussed in some of the articles is mostly speed control in the field of electric machines, where various algorithms have been used to optimize the considered controller, and always one of the most important challenges in this field is designing a controller with a high degree of freedom. In these researches, the focus is more on searching for an algorithm with more optimal results than others in order to estimate the parameters in a more appropriate way. There are many techniques for designing a PID controller. Among these methods, meta-innovative methods have been widely studied. In addition, the effectiveness of these methods in controlling systems has been proven. In this paper, a new method for grid control is discussed. In this method, the PID controller is used to control the power systems, which can be controlled more effectively, so that this controller has four parameters, and to determine these parameters, the optimization method and evolutionary algorithms of genetics (EGA) and PSO are used. One of the most important advantages of these algorithms is their high speed and accuracy. In this article, these algorithms have been tested on a single-machine system, so that the single-machine system model is presented first, then the PID controller components will be examined. In the following, according to the transformation function matrix and the relative gain matrix, suitable inputs for each of the outputs are determined. At the end, an algorithm for designing PID controller for multivariable MIMO systems is presented. To show the effectiveness of the proposed controller, a simulation was performed in the MATLAB environment and the results of the simulations show the effectiveness of the proposed controller.

Real-Time Effective Monitoring and Control in Oil and Gas Industry Using SCADA Technology as a Management Tool

Pipeline leakage detection, if not addressed swiftly/promptly, could result to loss of lives and properties. Researches have shown that the best means of curbing or drastically minimizing these losses is through real time monitoring using DCS and CAO-SCADA Technology as the management tool. This tool was adopted in this work and then applied to a particular oil and gas terminal in Nigeria. Research questions were generated and data obtained as well as the research instrument used were validated. A total of one hundred and thirty-five respondents were used for the study. From the formulated testing based on z-test, three hypotheses were investigated termed as HO1, HO2 and HO3. HO1 test hypothesis of pipeline failure as a result of leakages along it and if the leakage detection is because there is no DSC, CAO-SCADA management tool along the pipeline. The result obtained showed that z-calculated is less than the critical value of 1.96 with a significant level of 0.05. From the results obtained, the hypothesis HO1 is accepted, thus failure of this pipeline is because the DSO, CAO-SCADA is not present in the system. HO2 testing determined if there exists any difference significantly between the DSC and CAO-SCADA system. From the result, the z-calculated which is 0.0065 is less than the critical value which is 1. This implies that the null hypothesis (HO2) is accepted. Meaning that there is no significant difference between CAO-SCADA and the DCO for the effective management and monitoring and safety operations in the pipeline. The last hypothesis is to test if there exists significant difference between DCS and CAO-SCADA system for effective industrial system control of leakages in pipeline. Out of the 135(100%) respondents, 106(78.52%) agreed with the recommendations made by the researchers while 12.59% partially agreed. 4(5.18%) have a clear understanding and 5(0.0370%) are indifferent about the recommendations made.

Contamination control

Contamination in hydraulic and lube system fluids is generally recognised as the single most important factor in reducing the reliability and longevity of the equipment. This article examines how measurement, monitoring and control in industrial systems can ensure a sustained performance.

Advances in Fault Diagnostics and Post‐Fault Operation of Electrical Drives

Advances in Fault Diagnostics and Post‐Fault Operation of Electrical Drives

Reinforcement Learning-Based Composite Optimal Operational Control of Industrial Systems With Multiple Unit Devices

This article investigates the optimal operational control (OOC) problem for a class of industrial systems consisting of multiple unit devices with fast dynamics and an unknown operational process with slow dynamics. First, the OOC problem is formulated as a noncascade optimal control problem of two-time-scale systems with a novel performance function. Second, using singular perturbation theory, a decentralized composite control scheme is proposed by decomposing the original optimal problem into reduced-order fast and slow subsystem problems. Then, in the framework of reinforcement learning, an online controller design method for the slow subsystem is proposed by using the online measurement, and an offline controller design for the fast subsystem is proposed by using the unit device models. The obtained decentralized composite optimal controller achieves both the desired operational index tracking and disturbance rejection without requiring the dynamics of the operational process. Different from the existing cascade design methods, the proposed approach regulates the unit devices and operational process simultaneously, as well as overcomes the potential high dimensionality and ill-conditioned numerical issues. Finally, a mixed separation thickening process and a numerical example are given to illustrate the presented results.

Artificial Fuzzy-PID Gain Scheduling Algorithm Design for Motion Control in Differential Drive Mobile Robotic Platforms.

Mobile robots are promising devices which are dedicated to human comfort in all areas. However, the control algorithm of the wheels of mobile robot is entirely challenging due to the nonlinearity. Recently, the classical PID (proportional-integral-derivative) controllers are frequently used in robotics for their high accuracy and the smooth determination of their parameters. A robust approach called fuzzy control which is based on the conversion of linguistic inference sets in a suitable control value is a widely used method in industrial system control in our days. A new challenging method to solve the problem of intelligent navigation of nonholonomic mobile robot is suggested. In this work, the presented methodology is based on three hybrid fuzzy logic PID controllers which are adapted to guarantee target achievement and trajectory tracking. A fuzzy-PID control algorithm is designed with 2 inputs and 3 outputs. By the information given by the system response, error and error derivate can be used to extract and adopt the PID controller parameters: proportional, integral, and derivative gains. Besides, a tuning value A is introduced to improve the resulted response in terms of speeding up and reducing error, overshoot, and oscillation, as well as reducing ISE and IAE values. A modelization of a differential drive mobile robot is presented. The developed algorithm is tested and implemented to this mobile robot model via Simulink/MATLAB.

A Fault-Tolerant Control Approach Based on Image Processing Applied to Three-Tank System

This paper presents an innovative and practical solution for fault-tolerant control of industrial systems based on hybrid redundant measurements. The paper proposes a fault-tolerant control scheme against single and multiple sensor faults that can occur in typical tank-based industrial applications. A robust fault detection filter based on $H_{-} /H_{\infty }$ index is designed to generate a residual signal that is robust against disturbance and sensitive to the fault, simultaneously. It also integrates camera information and image processing techniques in the control scheme to get level-measurement redundancy. The proposed scheme was tested using the well-known three-tank benchmark, with both offline and online computation. Results showed that the proposed scheme tolerated the sensor faults and preserved the correct operation of the tank levels in both single and multiple-sensor faults, without changing the original structure of the controller.

Improved Control for Industrial Systems Over Model Uncertainty: A Receding Horizon Expanded State Space Control Approach

In this paper, an improved linear quadratic tracking control strategy using a new receding horizon expanded state space (ESS) model is proposed. Unlike traditional state space model, the new ESS model first facilitates the combination of the system state variables and tracking errors, then a subsequent new receding horizon improved linear quadratic tracking control (RHLQTC) is designed with more degrees of freedoms for the adjustment of control parameters, which yields improved control performance compared with traditional RHLQTC. Finally, simulations on a typical reverse process are done in comparison with traditional RHLQTC in terms of both servo and regulatory performance, where results show that the proposed controller provides improved performance under both situations.

Discrete Event System Control in Max-Plus Algebra: Application to Manufacturing Systems

Abstract This paper deals with control of industrial systems which can be depicted by timed event graphs. A methodology and software tools are presented to allow engineers to implement automatically controllers in PLC. Three steps are needed and recalled: the modelling of the system in max plus algebra, the design of the controller and the implementation in a Supervisory Control and Data Acquisition (SCADA) system. An example on a real system is given to illustrate these steps.

Anomaly Detection and Diagnosis in IIoT Systems: A Review of Techniques and Applications

Real-time monitoring and control of industrial systems are now feasible thanks to the IIoT. As a result, massive amounts of data have been produced, which may be used to better understand how the systems work. However, it may be challenging to find and analyse problems in data acquired by IIoT due to the richness and variety of the data. The continuing safety, dependability, and efficiency of IIoT systems rely on their capacity to identify and diagnose anomalies. Complex methods like machine learning algorithms, deep learning models, or hybrid strategies are needed for this purpose. In this research, we discuss the advantages and disadvantages of the various methods and approaches used for anomaly detection and diagnosis in IIoT systems. We also discuss the challenges and potential of this area of study, as well as their consequences. We also assess commonly used datasets and benchmarking systems, as well as real-world applications in a wide range of sectors, to gauge the efficacy of anomaly detection strategies. The purpose of this research is to offer a comprehensive overview of the essential themes in order to facilitate the development and deployment of effective and efficient anomaly detection and diagnostic systems in industrial settings.

Control of Industrial Systems to Avoid Failures: Application to Electrical System

We resolve the control problem for a class of dynamic hybrid systems (DHS) considering electrical systems as case study. The objective is to guarantee that the plan never reaches unsafe states. We consider a subclass class of DHS called Cumulative Preemptive Event-driven DHS (CPE-DHS). This class is distinguished by the dominance of its discrete aspect characterized by features as cumulative continuous variables combined with actions behavior that may be interrupted and restarted. We utilize a subclass of Rectangular Hybrid Automata (RHA), named Constant Slope RHA (CSRHA), as a solution framework to resolve the control problem. The main contribution is a control Algorithm for the class of systems described above. This algorithm ensures that the system meet the requirement specifications by forcing some events. The forcing action is given in the form of restrictions on the transition guards of the CSRHA. The termination/decidability as well as correctness of the algorithm is given by theorems and formal proofs. This contribution ensures that the system will always be safe states and avoid failure due to the reachability of unsafe states. Our approach can be applied to a large category of industrial systems, especially electrical systems that we consider as case study.

Mitigation of Fatigue Damage and Vibration Severity of Electric Drivetrains by Systematic Selection of Motion Profiles

The offshore drilling industry is among the most demanding markets for electrical equipment. Heave motion, irregular cyclic loads, harsh weather conditions, and vibrations are causing accelerated deterioration of drilling equipment. One of the most common solutions to these problems is to design actuation systems of such machinery overly conservative to gain additional safety, which results in too high initial investment and maintenance costs. To mitigate the fatigue damage and vibration severity of rotating elements of electric drivetrains operating offshore, this paper presents a comparative analysis of four popular input functions used in motion control of industrial systems. We evaluate them not only by using well-known performance indicators, such as maximum load or velocity, but also by assessing their influence on fatigue life and vibration severity of electric drivetrains. The rainflow counting algorithm is used to assign amplitudes and mean values of distinguished cycles from random loading history. Then, the Palmgren–Miner rule together with S–N curves are applied to determine the total damage for cycles with varying amplitudes. In addition, we quantify the cumulative effect of vibrations and jerk on machine damage by using the metric based on the jerk energy. The importance of this study is illustrated on a full-scale offshore pipe handling machine by benchmarking simulation results with the field data. The outcomes demonstrate not only a serious decrease in damage caused by vibrations for smooth motion profiles, but also provide a basis to formulate rules of thumb for selection of the most suitable motion profile for certain applications.

Review of Recent Type-2 Fuzzy Controller Applications

Type-2 fuzzy logic controllers (T2 FLC) can be viewed as an emerging class of intelligent controllers because of their abilities in handling uncertainties; in many cases, they have been shown to outperform their Type-1 counterparts. This paper presents a literature review on recent applications of T2 FLCs. To follow the developments in this field, we first review general T2 FLCs and the most well-known interval T2 FLS algorithms that have been used for control design. Certain applications of these controllers include robotic control, bandwidth control, industrial systems control, electrical control and aircraft control. The most promising applications are found in the robotics and automotive areas, where T2 FLCs have been demonstrated and proven to perform better than traditional controllers. With the development of enhanced algorithms, along with the advancement in both hardware and software, we shall witness increasing applications of these frontier controllers.

Chaotic particle swarm optimization for optimal design of PID controllers in industrial systems

Proportional integral derivative (PID) controllers are widely used in industrial control processes since they are simple and easy to implement and act as an effective measure to manipulate the dynamic properties of industry systems. Carrying out the optimal design of the PID controllers is an indispensable constituent of the premises of highly precious control of these systems. In order to solve the problem of designing the parameters of the PID controllers more effectively, we bring forward a chaotic particle swarm optimization (CPSO) approach which we call CPIDSO. In this approach, we introduce the combination of chaotic logistic dynamics, hierarchical inertia weight, enhancement learning strategy, mutation mechanism and a proportional integral derivative (PID) controller. The chaotic logistic map is used in the substitution of the two random parameters affecting the convergence behavior. The hierarchical inertia weight coefficients are determined in accordance with the present fitness values of the local best positions so as to adaptively expand the particles' search space. The PID controller and enhancement learning strategy are simultaneously incorporated into standard PSO (SPSO) to efficiently enhance the particles' local and global search exploration and exploitation abilities. For performance validation of CPIDSO, CPIDSO, together with other algorithms like chaotic catfish PSO (CCPSO), genetic algorithm (GA) and PSO, is exploited to design the parameters of a PID controller in a Kalman filter based cybernetic system. The simulation results illustrate that CPIDSO exhibits better performance than other algorithms and yields the best result in the parameter optimization design of the system.

Control of Industrial Drives through Multicell Impedance Source Inverter

In this paper, various power quality issues that occur while driving an industrial motor drive such as voltage sag, swell, interruptions are discussed. A solution for such issues is proposed drives during various conditions of power quality issues is discussed in detail and a comparison with traditional Z-source inverter is made. The results are validated for mathematically modelled using Multicell Z-source inverter. By controlling shoot-through duty cycle using various techniques such as Simple boost control, Maximum boost control, constant boost control these issues are addressed. Multicell Z-source inverter has come as a replacement for traditional Z-source inverter due to its less EMI noise; increased voltage level with increase in cell as well as reduced voltage stress makes it more advantages than traditional one. In this paper, control of industrial system of 3 kW induction drive in MATLAB/Simulink environment and the results are summarised.

Comparison of wireless sensor network and radio frequency identification for the process control of distributed industrial systems

In today’s demanding market, flexibility and reconfigurability play an important role in manufacturing industries. This can be achieved by employing distributed control system such as International Electrotechnical Committee 61499 function block, which is a new standard adopted for industrial-process measurement and control system. On the other hand, wireless systems have been adopted recently in manufacturing systems as a replacement for conventional wired systems. This includes the wireless sensor network and radio frequency identification, which are emerging technologies adopted recently in a wide range of applications, especially in industry sectors. This article presents and compares the application of wireless sensor network and radio frequency identification in the process control of industrial system on the basis of International Electrotechnical Committee 61499 standard. Two process control architectures are considered for a case of an assembly line in order to compare and measure performance by employing Petri net as a mathematical modelling tool.

From Traditional to Fractional PI Control: A Key for Generalization

Proportional-integral (PI) controllers are the most common form of feedback used in industrial applications today [1][3]. The use of proportional and integral feedback also has a long history of practical applications [4]. For example, in the middle of the 18th century, centrifugal governors as the proportional feedback were applied to regulate the speed of windmills [5]. By the 19th century, it was known that using integral feedback could remove the offsets appearing in working with governors [6]. At present, PI control, still a very basic form of feedback, is also one of the first solutions often considered in the control of industrial systems [7]. On the other hand, in some applications, using the PI controller in its traditional form may not be satisfactory, and a more advanced controller is needed to achieve control objectives. In such cases, modified versions of the PI controller have been proposed to enhance the controller's performance. The fractional-order PI controller is one of these modified versions, and it is attracting increased interest in control system design uses [8], [9]. The idea of using such a controller originated with fractional calculus, known as a generalization for classical calculus [10]. The following section presents a brief review of recent fractional calculus applications in control system design.