Control Systems Engineering Research Articles

Modeling and Simulation of Fractional PID Controller for Under-actuated Inverted Pendulum Mechanical System

The stabilization of the non-linear inverted pendulum system requires a robust control strategy, as this system is inherently unstable and sensitive to disturbances. This research utilizes Lagrangian mechanics, a powerful technique in analytical dynamics, to derive the mathematical representation of the system. By applying the principles of Lagrangian dynamics, we can accurately model the energies involved and derive the equations of motion that govern the pendulum’s behavior. Following this, state-space feedback is employed to determine the Proportional, Integral, and Derivative (PID) values essential for effective control. This control strategy is particularly useful due to its ability to minimize error over time and ensure stability. To further enhance the control process, a comprehensive mathematical model is developed to establish the transfer function that correlates the pendulum's angle with the displacement of the cart. This relationship is crucial for understanding how changes in the cart's position affect the pendulum's stability. To validate the proposed control law, extensive simulations are conducted, allowing for comparative analysis against an Integer Order Controller. These simulations not only highlight the effectiveness of the PID controller but also provide insights into the dynamic behavior of the system under various conditions. The results demonstrate significant improvements in settling time and overshoot, showcasing enhanced performance metrics for the selected objective functions. This research contributes to the broader field of control systems engineering, suggesting that advanced control strategies can effectively manage complex, non-linear systems.

Variable target water level control method in front of sluice in flood storage area based on storage balance model

Abstract To prevent the increase in flood disaster frequency, scale, and impact degree, a flood control engineering system with flood storage and detention areas, embankments, and reservoirs as the main body has been formed. Flood storage area is the key to the flood control engineering system, and its water level control performance is directly related to the success of flood control. According to the actual situation, the flood evolution simulation model in the flood storage area is constructed to obtain the evolution information of any unit of flood in the flood storage area. The dynamic control judgment coefficient of the water level is calculated, and the dynamic control program of the variable target water level in front of the sluice is formulated. Water level control can be realized by executing the established procedures. The above experimental data show that the proposed method can effectively control the water level in the flood storage area within the range of 26.03m ∼ 29.12m, and the minimum value of the corresponding risk rate is 3%, which fully proves that the proposed method has better dynamic control effect on the variable target water level in front of the sluice.

Performance Evaluation of Fractional Proportional–Integral–Derivative Controllers Tuned by Heuristic Algorithms for Nonlinear Interconnected Tanks

This article presents an in-depth analysis of three advanced strategies to tune fractional PID (FOPID) controllers for a nonlinear system of interconnected tanks, simulated using MATLAB. The study focuses on evaluating the performance characteristics of system responses controlled by FOPID controllers tuned through three heuristic algorithms: Ant Colony Optimization (ACO), Grey Wolf Optimizer (GWO), and Flower Pollination Algorithm (FPA). Each algorithm aims to minimize its respective cost function using various performance metrics. The nonlinear model was linearized around an equilibrium point using Taylor Series expansion and Laplace transforms to facilitate control. The FPA algorithm performed better with the lowest Integral Square Error (ISE) criterion value (297.83) and faster convergence in constant values and fractional orders. This comprehensive evaluation underscores the importance of selecting the appropriate tuning strategy and performance index, demonstrating that the FPA provides the most efficient and robust tuning for FOPID controllers in nonlinear systems. The results highlight the efficacy of meta-heuristic algorithms in optimizing complex control systems, providing valuable insights for future research and practical applications, thereby contributing to the advancement of control systems engineering.

An engineering perspective on transcription, translation and their regulation.

Information coded in DNA is replicated, modified and transmitted from the origins of protein-based life. Analogies of these processes to information processing, transmission and storage in computer systems is straightforward and can be utilized both in analysis of biological data and in development of biologically based technical systems. Transcription and translation processes are regulated by extremely complex regulatory networks, providing control of cell growth, cell cycle and cellular responses to stress. As such, they constitute engineering control systems exerting their actions at many levels of time scale and spatial organization. This work presents an engineering perspective on DNA-related information processing and biochemical process control in living cells, followed by a review of two-way crosstalk between engineering and biology.

Imperative Formal Knowledge Representation for Control Engineering: Examples from Lyapunov Theory

In this paper, we introduce a novel method to formally represent elements of control engineering knowledge in a suitable data structure. To this end, we first briefly review existing representation methods (RDF, OWL, Wikidata, ORKG). Based on this, we introduce our own approach: The Python-based imperative representation of knowledge (PyIRK) and its application to formulate the Ontology of Control Systems Engineering (OCSE). One of its main features is the possibility to represent the actual content of definitions and theorems as nodes and edges of a knowledge graph, which is demonstrated by selected theorems from Lyapunov’s theory. While the approach is still experimental, the current result already allows the application of methods of automated quality assurance and a SPARQL-based semantic search mechanism. The feature set of the framework is demonstrated by various examples. The paper concludes with a discussion of the limitations and directions for further development.

Role of ultrasensitivity in biomolecular circuitry for achieving homeostasis

Living systems have developed control mechanisms for achieving homeostasis. Here, we propose a plausible biological feedback architecture that exploits ultrasensitivity and shows adaptive responses without requiring error detection mechanism (i.e., by measuring an external reference signal and deviation from this). While standard engineering control systems are usually based on error measurements, this is not the case for biological systems. We find that a two-state negative feedback control system, without explicit error measurements, is able to track a reference signal that is implicitly determined by the tunable threshold and slope characterizing the sigmoidal ultrasensitive relationship implemented by the control system. We design different ultrasensitive control functions (ultrasensitive up- or down-regulation, or both) and, by performing sensitivity analysis, show that increasing the sensitivity level of the control allows achieving robust adaptive responses to the effects of parameter variations and step disturbances. Finally, we show that the devised control system architecture without error detection is implemented within the yeast osmoregulatory response network and allows achieving adaptive responses to osmotic stress, by exploiting the ubiquitous ultrasensitive features of the involved biomolecular circuitry.

Public Decision Policy for Controlling COVID-19 Outbreaks Using Control System Engineering

This work is a response to the appeal of various international health organizations and the Automatic Control Community for collaboration in addressing Coronavirus/COVID-19 challenges during the initial stages of the pandemic. Specifically, this study presents scientific evidence supporting the efficacy of three primary non-pharmacological strategies for pandemic mitigation. We propose a control system to aid in formulating a public decision policy aimed at managing the spread of COVID-19 caused by the SARS-CoV-2 virus, commonly known as coronavirus. The primary objective is to prevent overwhelming healthcare systems by averting the saturation of intensive care units (ICUs). In the context of COVID-19, understanding the peak infection rate and its time delay is crucial for preparing healthcare infrastructure and ensuring an adequate supply of intensive care units equipped with automatic ventilators. While it is widely recognized that public policies encompassing confinement and social distancing can flatten the epidemiological curve and provide time to bolster healthcare resources, there is a dearth of studies examining this pivotal issue from the perspective of control system theory. In this study, we introduce a control system founded on three prevailing non-pharmacological tools for epidemic and pandemic mitigation: social distancing, confinement, and population-wide testing and isolation in regions experiencing community transmission. Our analysis and control system design rely on the susceptible-exposed–infected–recovered–deceased (SEIRD) mathematical model, which describes the temporal dynamics of a pandemic, tailored in this research to account for the temporal and spatial characteristics of SARS-CoV-2 behavior. This model incorporates the influence of conducting tests with subsequent population isolation. An On–off control strategy is analyzed, and a proportional–integral–derivative (PID) controller is proposed to generate a sequence of public policy decisions. The proposed control system employs the required number of critical beds and ICUs as feedback signals and compares these with the available bed capacity to generate an error signal, which is utilized as input for the PID controller. The control actions outlined involve five phases of “Social Distancing and Confinement” (SD&C) to be implemented by governmental authorities. Consequently, the control system generates a policy sequence for SD&C, with applications occurring on a weekly or biweekly basis. The simulation results underscore the favorable impact of these three mitigation strategies against the coronavirus, illustrating their efficacy in controlling the outbreak and thereby mitigating the risk of healthcare system collapse.

Utilization of G-Programming Language for Educational Control Application: Case Study of Magnetic Levitation of Elastic Beam

This paper explores the application of G-Programming tools in designing and implementing traditional control algorithms for a magnetic levitation system using LabVIEW. The study focuses on the challenges of managing this fast, dynamic, and sensitive system, demonstrating the effcacy of LabVIEW G-Programming for control education. PID and Lead-Lag controllers are developed within LabVIEW, featuring tuning and optimization capabilities through Virtual Instruments of the control design and simulation toolkit. The research provides insights into the system’s modelling, control action testing, dynamic simulation, and real-time control law deployment, concluding that G-Programming effectively facilitates experiential learning and validation in control systems engineering.

BLDC Motor Stability Management Using Adaptive PID (MRAC-PID)

BLDC motors have become popular in various industries such as automotive, consumer, healthcare, industrial automation, and instrumentation due to their optimal performance. To keep the BLDC motor in optimal condition, a control engineering system is required that serves as a controller. A single Proportional Integral Derivative (PID) control system is only suitable for linear conditions, so it cannot produce satisfactory output when there is a change in set point. To overcome this obstacle, an adaptive PID control system known as MRAC-PID control system is applied, which is able to control the stability of the BLDC motor as desired. Testing of this system is done under 4 different conditions using MATLAB software. After testing, the parameter values for the MRAC control system were obtained, namely ???????? = 0.4; ???????? = 50.75; ???????? = 0.0000867. Based on the test, the MRAC control system produces a system success rate of 81.2% to 98.9%.

The Modeling and Control of (Renewable) Energy Systems by Partial Differential Equations—An Overview

Mathematical models of energy systems have been mostly represented by either linear or nonlinear ordinary differential equations. This is consistent with lumped-parameter dynamic system modeling, where dynamics of system state variables can be fully described only in the time domain. However, when dynamic processes of energy systems display both temporal and spatial evolutions (as is the case of distributed-parameter systems), the use of partial differential equations is necessary. Distributed-parameter systems, being described by partial differential equations, are mathematically (and computationally) much more difficult for modeling, analysis, simulation, and control. Despite these difficulties in recent years, quite a significant number of papers that use partial differential equations to model and control energy processes and systems have appeared in journal and conference publications and in some books. As a matter of fact, distributed-parameter systems are a modern trend in the areas of control systems engineering and some energy systems. In this overview, we will limit our attention mostly to renewable energy systems, particularly to partial differential equation modeling, simulation, analysis, and control papers published on fuel cells, wind turbines, solar energy, batteries, and wave energy. In addition, we will indicate the state of some papers published on tidal energy systems that can be modelled, analyzed, simulated, and controlled using either lumped or distributed-parameter models. This paper will first of all provide a review of several important research topics and results obtained for several classes of renewable energy systems using partial differential equations. Due to a substantial number of papers published on these topics in the past decade, the time has come for an overview paper that will help researchers in these areas to develop a systematic approach to modeling, analysis, simulation, and control of energy processes and systems whose time–space evolutions are described by partial differential equations. The presented overview was written after the authors surveyed more than five hundred publications available in well-known databases such as IEEE, ASME, Wiley, Google, Scopus, and Web of Science. To the authors’ best knowledge, no such overview on PDEs for energy systems is available in the scientific and engineering literature. Throughout the paper, the authors emphasize novelties, originalities, and new ideas, and identify open problems for future research. To achieve this goal, the authors reviewed more than five hundred journal articles and conference papers.

Feasibility Study of Developing Ship Engineering Control System based on DDS Middle-ware

Feasibility Study of Developing Ship Engineering Control System based on DDS Middle-ware

Handling and Control of Floods in the Pulukan River/Tukad Macro Drainage System in Jembrana Regency

Handling and controlling floods in the river/Tukad macro drainage system as the main discharge is a very complex matter. The engineering dimension (engineering) involves many disciplines. In this study, the analytical review is only on hydrological boundaries, hydraulics, watershed erosion, river engineering, morphology, river sedimentation, handling engineering, and flood control systems. The stages of compiling this research were preceded by the collection of secondary and primary data which were then carried out by analysis. The analysis in this planning is carried out qualitatively, quantitatively, and descriptively. The qualitative description focuses on inventorying the drainage system, determining the zoning of the drainage system based on watershed boundaries, class, and identification of area/area per unit drainage system. The quantitative analysis focuses on hydrological and hydraulic calculations as well as a detailed design of each segment of the drainage channel by the service area of ​​each drainage system. Furthermore, the descriptive analysis focuses on determining the priority scale for the handling of the drainage system which is considered vulnerable to flood hazards in the downstream area. Besides that, it also provides technical and non-technical recommendations for flood management. The results of the analysis of the sedimentation rate of the Pulukan River/Tukad 384.726 tons/ha/year are included in the class of severe erosion hazard because most of the community gardening activities are at an elevation of 25% to 40% which has an impact on the high LS slope factor. The existing cross-sectional capacity of the Pulukan River/Tukad is not able to accommodate the flood discharge planned for a return period of 50 years. From the results of this analysis, it is necessary to carry out flood handling/control using structural and non-structural methods. The Tukad Pulukan Watershed flood management and control program includes; reforestation, river normalization work, river management building, Tukad Pulukan watershed management, community-based watershed management, land use regulation, construction of access road inspections, installation of recording of water level and density of recording stations rainfall.

Feedback control applied to a low cost ball-in-tube air levitation plant

Commercial educational tools for teaching feedback concepts in control systems engineering and mechatronics are generally expensive, large, complexand sensitive instruments; therefore, it cannot be used in universities with budget constraints, which greatly hinders the teaching of basic concepts of control theory and application. The mathematical tools that are taught in the disciplines of control theory in the undergraduate course are often abstract, especially in terms of practical application in an industrial setting, which makes it necessary to use didactic control plants to complement teaching and hands-on experimentation. This paper presents a low-cost ball and tube air levitation laboratory system as a teaching tool for engineering. Ball and tube laboratory setup is a dynamic benchmark system, designed to control the position of the ball on a vertical upward airflow that counteracts the gravitational force exerted on the ball without mechanical support. A blower feeds airflow, and the position of the ball is measurable by using an ultrasonic distance meter. As the purpose of the article is to serve as support material for undergraduate students who are taking their first steps in the study of control theory and feedback control, detailed modeling by the laws of physics are presented, followed by classic tools for the study of linear control and then a proportional-integral-derivative (PID) control system is developed. A modified structure of the classic PID is used and the results are compared with the classic structure.

Design and performance analysis of a novel vulture based FOPID controller for the nonlinear biological system

In recent times, the field of Automatic Control Systems Engineering engaged in regulating large number of biological processes has played a significant role in biological and biotechnology applications. In human body, the very most important biological parameters are pancreas, insulin, glucose and genetic regulatory network (GRN). Any abnormal change in these biological system parameters may leads to occurrence of severe diseases and finally leads to the death of the infected patient. Thus, to tune the biological parameters in the human body, an efficient optimal controller is essential. Hence, a novel hybrid controller called Vulture based Fractional Order Proportional-Integral-Derivative (VbFOPID) controller is designed and implemented in this paper. The designed controller is implemented in MATLAB platform. From the obtained simulation results and its comparisons with the other existing controller's in the literature, it clearly presents that the proposed VbFOPID controller has superior performance over the other optimal PID controller designs in terms of rise time, convergence time, settling time, error percentage and maximum overshoot.

Bayesian, Multifidelity Operator Learning for Complex Engineering Systems–A Position Paper

Abstract Deep learning has significantly improved the state-of-the-art in computer vision and natural language processing, and holds great potential to design effective tools for predicting and simulating complex engineering systems. In particular, scientific machine learning seeks to apply the power of deep learning to scientific and engineering tasks, with operator learning (OL) emerging as a particularly effective tool. OL can approximate nonlinear operators arising in complex engineering systems, making it useful for simulating, designing, and controlling those systems. In this position paper, we provide a comprehensive overview of OL, including its potential applications to complex engineering domains. We cover three variations of OL approaches: deterministic OL for modeling nonautonomous systems, OL with uncertainty quantification (UQ) capabilities, and multifidelity OL. For each variation, we discuss drawbacks and potential applications to engineering, in addition to providing a detailed explanation. We also highlight how multifidelity OL approaches with UQ capabilities can be used to design, optimize, and control engineering systems. Finally, we outline some potential challenges for OL within the engineering domain.

Research on the autonomous train control system based on vehicle-to-vehicle communication

In recent years, there has been a significant increase in the importance of rail transit, which is largely due to the development of cities. It is anticipated that a new generation of train control systems will be required as the number of passengers increases and track resources become more limited. Seven driving modes of the autonomous train control system based on vehicle-to-vehicle communication are studied in four aspects: system architecture, control method, driving mode conversion, and system safety analysis. By analyzing the correlation between each operating mode in the normal and abnormal operations scenarios, and taking safety into consideration, this paper proposes a train driving mode conversion scheme that covers the whole scenario of train operation by taking safety into consideration in both normal and abnormal operations. This virtual coupling technology further reduces the overall system headway, thereby allowing for more efficient train control systems to be developed, and is intended to provide a useful reference for the engineering of new train control systems.

Systems Engineering Approach to Modeling and Analysis of Chronic Obstructive Pulmonary Disease.

Chronic obstructive pulmonary disease (COPD) is a progressive lung disease characterized by airflow limitation. This study develops a systems engineering framework for representing important mechanistic details of COPD in a model of the cardiorespiratory system. In this model, we present the cardiorespiratory system as an integrated biological control system responsible for regulating breathing. Four engineering control system components are considered: sensor, controller, actuator, and the process itself. Knowledge of human anatomy and physiology is used to develop appropriate mechanistic mathematical models for each component. Following a systematic analysis of the computational model, we identify three physiological parameters associated with reproducing clinical manifestations of COPD: changes in the forced expiratory volume, lung volumes, and pulmonary hypertension. We quantify the changes in these parameters (airway resistance, lung elastance, and pulmonary resistance) as the ones that result in a systemic response that is diagnostic of COPD. A multivariate analysis of the simulation results reveals that the changes in airway resistance have a broad impact on the human cardiorespiratory system and that the pulmonary circuit is stressed beyond normal under hypoxic environments in most COPD patients.

124 Laboratory Evaluation of Low-Cost Optical Particle Counters for Occupational Respirable Exposure Measurements

Abstract Direct-reading, time-resolved devices, such as optical particle counters (OPCs) and photometers, offer a unique insight into the temporal and spatial distribution of airborne particles. They can provide a comprehensive picture of changes in concentration of airborne particles in occupational settings and therefore can be used to investigate failures in engineering control systems as well as identify exposures driven by working procedures and methods. In recent years, new developments have led to the commercialisation of low- cost optical-based sensors, which provide particle matter (PM) mass concentrations including PM2.5 and PM10 for environmental monitoring. TNO, NIOSH, and HSE are investigating their application to occupational settings with the aim to produce guidelines for calibration and use. This study evaluated the performance and accuracy of six commercially available low-cost sensors (Airbeam 2, Airveda, Omni Awair, OPC-N3, OPC-R1 and PATS+), in calm air test chambers, against reference devices including an Aerodynamic Particle Sizer (APS 3320), GK2.69 respirable cyclones, and pDR-1500 photometers. Several factors were considered: type of dust (particles having different size distribution, shape and refractive index), within- and between-device variations and exposure pattern (peak and constant concentrations). The devices were subjected to relatively high respirable concentrations (greater than 1 mg/m3) in addition to low concentrations. This presentation will present the results of the laboratory testing with particular focus on their accuracy, response, and calibration for quantitative exposure measurements. The low-cost sensor devices are also being deployed in the workplace for further evaluation and practicality of use.

English

Global economy is facing a recession, and there is wide fluctuation in energy demand. This fact is forcing the projects to become more cost competitive while reducing the project schedule. Projects in Oil & Gas sectors have increased dramatically in scale and complexity, whereas there is hardly any additional time allocated to complete these projects. Automation hardware & software has come under more pressure due to the dependency on design data from other disciplines. With increased complexity, input data continue to change throughout the project design cycle. Addressing these concerns has become a major challenge for control system design engineers. This is where the Smart enclosures, in combination with the new IO technologies like foundation fieldbus, remote IO, smart IOs etc., become handy to control system engineers. The automation industry has evolved itself and is continuously upgrading the systems to mitigate the cost, allocate design bandwidth along with the management of last-minute changes. This paper will mainly discuss about the various issues and concerns faced by control system design engineers. It will mainly focus on how smart enclosures with smart IO/new technology provides a cost-effective solution, flexibility and ease of maintenance while also addressing the main challenges discussed above. The paper will briefly discuss the main problems faced during various faces of the project and how smart enclosures with new technologies address each of these issues. This paper will also analyze examples of how these advancements help in reducing the footprint, cost & schedules. The authors will also provide the details of the available options in the market and will share their experience on the projects using the smart enclosures and how it addressed the typical problems faced during the project life cycle.

Reinforcement learned adversarial agent (ReLAA) for active fault detection and prediction in space habitats

With growing interest for human space tourism in the twenty-first century, much attention has been directed to the robust engineering of Environmental Control and Life Support Systems in space habitats. The stable, reliable operation of such a habitat is partly achieved with an ability to recognize and predict faults. For these two purposes, a reinforcement learning adversarial agent (ReLAA) is utilized in this work. A ReLAA is trained with experimental data to actively recognize and predict faults. These capabilities are achieved by proposing actions that activate known faults in a system. Instead of issuing these harmful actions to the actual hardware, a digital twin of the mock space habitat is simulated to discover vulnerabilities that would lead to faulted operation. The methods developed in this work will allow for the discovery of damaging latent behavior, and the reduction of false positive and negative fault identification.