Control Systems Engineering Research Articles

General model of a transport-technological grain store route node for control systems programs

The paper concerns a problem to develop software for engineering control systems based upon standard industrial controllers and SCADA means at enterprises for grain storage and processing. The problem generalizing approaches to develop algorithms controlling equipment as a part of technological routes has been considered. A system to control transport-technological grain route at grain storage is the research object. Control of transport-technological equipment at a route level is the research subject. 
 Purpose is to develop general control model for nodes of a network of transport-technological grain storage routes to formalize tasks of software creation for industry specific engineering systems.
 Analysis, classification, and generalization of processing equipment have been carried out; approaches to implement functional structures of elevator routes have been studied. General model to control transport-technological grain storage route has been proposed in the form of a graph of states. Transitional conditions in the form of algebraic logic have been developed for this graph as well as algorithms to control certain mechanisms in terms of transitional states of the route.
 The control model developed in the form of graph involving transitional conditions and algorithms of transitional states has obtained its software implementation; moreover, it has been introduced in the context of industry specific engineering control systems at operating enterprises of grain storage and processing. The introduction results have demonstrated the model stability under the conditions of different enterprises engaged in grain storage and processing from the viewpoint of its implementation in the system software.
 The realized field experiments have demonstrated the system effectiveness, which allows to recommend it to solve the problems of software development for engineering control systems based upon standard industrial controllers and SCADA.

Fuzzy based incremental conductance algorithm stabilized by an optimal integrator for a photovoltaic system under varying operating conditions

ABSTRACT In renewable system control engineering, Maximum Power Point Tracking (MPPT) techniques are considered as one of the key steps in photovoltaic (PV) system development. Indeed, some MPPT techniques provide high performance under uniform irradiation and temperature conditions. However, their performance significantly degrades when the two preceding climatic conditions suddenly change. Therefore, this paper proposes a new voltage regulation based on Fuzzy Logic Control (FLC) strategy and small-signal model of DC–DC booster converter. The proposed control scheme is mainly based on the indirect incremental conductance (Inc) technique, equipped with an optimal integrator controller (OIC). It is implemented in a PV system, consisting of a DC–DC boost converter connected to a resistive load, with the main objective of ensuring a good MPP tracking behavior for variable operating conditions. To achieve this objective, the proposed MPPT strategy is designed around two stages. First, the smooth reference voltage is ensured by a Single-Input Single-Output (SISO) fuzzy logic controller using the Inc technique based on the MPPT approach. Then, the optimal integrator controller stabilizing the closed-loop system is integrated with optimization of its parameter according to Routh’s criterion for resistive load variations. Simulation results confirm the effectiveness of the proposed MPPT technique compared to some commonly used MPPT approaches. The superiority of the proposal is proven by using efficiency ratio, which rises more than 98% in all case studies. These objectives are ensured for both uniform and critical sudden change in weather conditions, regardless of resistive load variations.

Constrained model-free reinforcement learning for process optimization

Reinforcement learning (RL) is a control approach that can handle nonlinear stochastic optimal control problems. However, despite the promise exhibited, RL has yet to see marked translation to industrial practice primarily due to its inability to satisfy state constraints. In this work we aim to address this challenge. We propose an “oracle”-assisted constrained Q-learning algorithm that guarantees the satisfaction of joint chance constraints with a high probability, which is crucial for safety critical tasks. To achieve this, constraint tightening (backoffs) are introduced and adjusted using Broyden’s method, hence making the backoffs self-tuned. This results in a methodology that can be imbued into RL algorithms to ensure constraint satisfaction. We analyze the performance of the proposed approach and compare against nonlinear model predictive control (NMPC). The favorable performance of this algorithm signifies a step towards the incorporation of RL into real world optimization and control of engineering systems, where constraints are essential.

Application of Computer Control System in Electrical Automation

With the improvement of information technology, China has continuously promoted the development of computer control system of electrical engineering and electrical automation in the current development process. In enterprise production, computer technology can be integrated into electrical engineering and electrical automation to realize real-time monitoring of controlled objects. Electrical engineering automation belongs to the field of modern electrical information, which is widely used in military, aerospace and industry. However, due to the late formation of China’s industrial system, there are still some problems in electrical engineering automation, resulting in serious energy consumption problems. In order to improve the application level of electrical automation control system and create greater benefits for enterprises and society, it is necessary to strengthen the application research of electrical engineering and automation computer control system. Based on the application status of computer control system in electrical automation, this paper discusses the application strategy of computer control system in electrical engineering and electrical automation to promote the further development of electrical engineering technology in China.

Perfect adaptation in biology.

A distinctive feature of many biological systems is their ability to adapt to persistent stimuli or disturbances that would otherwise drive them away from a desirable steady state. The resulting stasis enables organisms to function reliably while being subjected to very different external environments. This perspective concerns a stringent type of biological adaptation, robust perfect adaptation (RPA), that is resilient to certain network and parameter perturbations. As in engineered control systems, RPA requires that the regulating network satisfy certain structural constraints that cannot be avoided. We elucidate these ideas using biological examples from systems and synthetic biology. We then argue that understanding the structural constraints underlying RPA allows us to look past implementation details and offers a compelling means to unravel regulatory biological complexity.

A multistage successive approximation method for Riccati differential equations

Riccati differential equations have played important roles in the theory and practice of control systems engineering. Our goal in this paper is to propose a new multistage successive approximation method for solving Riccati differential equations. The multistage successive approximation method is derived from an existing piecewise variational iteration method for solving Riccati differential equations. The multistage successive approximation method is simpler in terms of computing implementation in comparison with the existing piecewise variational iteration method. Computational tests show that the order of accuracy of the multistage successive approximation method can be made higher by simply taking more number of successive iterations in the multistage evolution. Furthermore, taking small size of each subinterval and taking large number of iterations in the multistage evolution lead that our proposed method produces small error and becomes high order accurate.

Research on Biological Protection Engineering Construction on Downstream Levee of the Yellow River

As an integral part of flood control engineering system, biological protection measures have always been attached great importance to by management units at all levels. Taking the calculation of the width of the breaktide belt in the lower reaches of the Yellow River as an example, this paper studies the great role of the protection works in ensuring the safety of the Yellow River project protection and the construction of water ecological civilization.

A modification of fuzzy arithmetic operators for solving near-zero fully fuzzy matrix equation

Matrix equations have its own important in the field of control system engineering particularly in the stability analysis of linear control systems and the reduction of nonlinear control system models. There are certain conditions where the classical matrix equation are not well equipped to handle the uncertainty problems such as during the process of stability analysis and reduction in control system engineering. In this study, an algorithm is developed for solving fully fuzzy matrix equation particularly for ~ A ~X ~B 􀀀 ~X = ~ C, where the coefficients of the equation are in near-zero fuzzy numbers. By modifying the existing fuzzy multiplication arithmetic operators, the proposed algorithm exceeds the positive restriction to allow the near-zero fuzzy numbers as the coefficients. Besides that, a new fuzzy subtraction arithmetic operator has also been proposed as the existing operator failed to satisfy the both sides of the nearzero fully fuzzy matrix equation. Subsequently, Kronecker product and V ec-operator are adapted with the modified fuzzy arithmetic operator in order to transform the fully fuzzy matrix equation to a fully fuzzy linear system. On top of that, a new associated linear system is developed to obtain the final solution. A numerical example and the verification of the solution are presented to demonstrate the proposed algorithm.

A Comprehensive Review of the Evolution of Networked Control System Technology and Its Future Potentials

Networked control systems (NCSs) are attracting the attention of control system engineers. The NCS has created a paradigm shift in control system technology. An NCS consists of control loops joined through communication networks in which both the control signal and the feedback signal are exchanged between the system and the controller. However, its materialization faces several challenges as it requires the integration of advanced control and communication techniques. This paper presents an extensive review of NCSs from the perspective of control system design. The evolution of NCSs is broadly divided in three phases, namely NCSs prior to 2000, NCSs during 2001–2010, and NCSs from 2011 onwards. This division corresponds to the initial status, intermediate status, and the recent status of the developments in the design of NCSs. The advancement of different control techniques during these phases has been discussed comprehensively. This paper also describes the transition of control systems form continuous domain to networked domain, which makes it better than the traditional control systems. Some important practical applications, which have been implemented using NCSs, have also been discussed. The thrust areas for future research on NCS have also been identified.

Advanced Mathematics and Computational Applications in Control Systems Engineering

Control-systems engineering is a multidisciplinary subject that applies automatic-control theory to design systems with desired behaviors in control environments [...]

Understanding rapid charge and discharge in nano-structured lithium iron phosphate cathodes

A Doyle–Fuller–Newman (DFN) model for the charge and discharge of nano-structured lithium iron phosphate (LFP) cathodes is formulated on the basis that lithium transport within the nanoscale LFP electrode particles is much faster than cell discharge, and is therefore not rate limiting. We present some numerical solutions to the model and show that for relevant parameter values, and a variety of C-rates, it is possible for sharp discharge fronts to form and intrude into the electrode from its outer edge(s). These discharge fronts separate regions of fully utilised LFP electrode particles from those that are not. Motivated by this observation an asymptotic solution to the model is sought. The results of the asymptotic analysis of the DFN model lead to a reduced order model, which we term the reaction front model (or RFM). Favourable agreement is shown between solutions to the RFM and the full DFN model in appropriate parameter regimes. The RFM is significantly cheaper to solve than the DFN model, and therefore has the potential to be used in scenarios where computational costs are prohibitive, e.g. in optimisation and parameter estimation problems or in engineering control systems.

Inelastic Analysis of Mdof Systems Damaged by Earthquakes, Posteriorly Subjected to Wind Load

This paper deals with the analysis of the inelastic response of buildings originally damaged by earthquakes and subjected to earthquake aftershock and wind loading. The overall aim is to establish the effect of wind actions on structural stability. To that end, one four-story bare frame benchmarked by the European Laboratory for Structural Assessment, is subject to various levels of winds and earthquake joint load while monitoring changes on the ductility demand. In this paper is shown that the combined action of strong winds and earthquakes, however its low probability of occurrence, would cause a decrease of strength reduction factors and considerably increase the ductility demand of damaged infrastructure hence inducing additional risks that would otherwise remain unquantified. The paper examines the non-linear performance of Multi-degree of freedom systems subject to various levels of winds and earthquake load and deals with the estimation of strength reduction factors. This is a relatively unexplored area of research which builds on past developments whereby inelastic performance of buildings has been discussed. It also links to various other paths of development such as structural reliability, forensic and control systems engineering. Doi: 10.28991/cej-2021-03091675 Full Text: PDF

CFD simulation for dispersion of benzene at a petroleum refinery in diverse atmospheric conditions.

Atmospheric parameters play a vital role in the dispersion of air pollutants. Benzene is a confirmed human carcinogen. It is also a neurotoxin and an irritant compound. The objective of this study was to examine the CFD simulation by Fluent16 software to simulate and analyze the effect of atmospheric conditions on the dispersion of benzene in eight different scenarios in a petroleum refinery. According to the results of this study, the highest and lowest impacts of atmospheric parameters occurred on spring days and autumn nights, respectively. Wind direction did not have a significant effect on the benzene distribution due to the artificial ceiling of piping installations in the computational domain. However, the wind speed had a critical role in the benzene dispersion. The maximum concentration occurred at 36- to 37-m distance from the inlet boundary for all scenarios except winter nights. On winter nights, this distance increased to 38 m. Benzene concentrations were the highest at their sources of release. They decreased after the artificial ceiling of the pipelines was at 5.5- to 7-m height where the air displacement was not sufficient, and therefore, leading to a gradual reduction in concentration. The accumulation of benzene concentration in the small domain was noticeable compared to the benzene concentration distributed in the total computational domain, and the authors recommended control measures in this domain. This study demonstrated CFD simulation methodology could enable the investigators to predict the benzene concentration dispersion in the atmosphere of a petroleum refinery plant. These findings can be used by occupational health engineers for health risk assessment of refinery personnel involved with maintenance operations and engineering control systems.

The PINNs method discovery to the solution of coupled Wave- Klein-Gordon equations

Recently, the research of PDEs is regarded as one of the most important disciplines. Almost all scientific problems can be described by a differential equation, especially, many physical phenomena can be described by the system of coupled Wave-Klein-Gordon equations, which plays an important role in high-performance computing, control engineering, and electronic power system. Consequently, in our work, we use the PINNs to solve the numerical solution of coupled Wave-Klein-Gordon equations, to help us better understand the nonlinear physical phenomena, and to promote the rapid development of various fields such as in high-performance computing, control engineering, and electronic power system.

Analytical Design of PID Controllers [Bookshelf

This book is focused exclusively on the PID controller. It is timely and contributes substantially to the art and science of control system engineering. The book proposes a multiobjective approach to design, allowing the designer to creatively blend various performance measures using classical and modern approaches. It is organized into three parts. Part I lays the foundation for the rest of the book. It describes methods for the computation of the complete set of PID controllers stabilizing a given SISO linear time-invariant plant. Using the stabilizing set, Part II proceeds to search for achievable performance for the closed-loop system under PID control. Finally, Part III adresses H3 optimization-based design of PID controllers for both continuous and discrete-time systems is considered. The book presents several novel and effective approaches for the synthesis and design of PID controllers. It addresses and solves classical gain and phase margin-based designs, modern H3 norm-based designs, and new approaches to multivariable control based on SISO methods. Both continuous-time and discrete-time systems are treated by the same methods. The methodology developed is based on research conducted at Texas A&M University over the last 20 years. It is expected that these approaches will find applications to control system design in both traditional areas as well as in new areas such as driverless cars and autonomous robots. Overall, the techniques covered in this book form a substantial contribution to the field of control, and this book is recommended for students and practitioners of control theory.

Simultaneous Process Design and Control Optimization using Reinforcement Learning

Abstract The performance of a chemical plant is highly affected by its design and control. A design cannot be accurately evaluated without its controls and vice versa. To optimally address design and control simultaneously, one must formulate a bi-level mixed-integer nonlinear program with a dynamic optimization problem as the inner problem; this is intractable. However, by computing an optimal policy using reinforcement learning, a controller with a closed-form expression can be computed and embedded into the mathematical program. In this work, an approach that uses a policy gradient method to compute the optimal policy, which is then embedded into the mathematical program is proposed. The approach is tested in a tank design case study and the performance of the controller is evaluated. It is shown that the proposed approach outperforms current state-of-the-art control strategies. This opens a whole new range of possibilities to address the simultaneous design and control of engineering systems.

Current Situation Analysis and Future Development Trend of Automotive Engineering Control System

With the development of electronic technology, the application of electronic control system (CS) in automobile is more and more, especially the dynamic CS. As a typical dynamic CS, ESC not only plays a key role in the field of active safety, but also is the key executive layer of intelligent driving system. Vehicle state parameters are the basis of ESC control and have a direct impact on the intervention effect of ESC related functions. From the cost of engineering application, a considerable part of the state parameters can not be directly measured by sensors. Therefore, the real-time and accurate estimation of state parameters becomes the key to limit the industrial application of ESC. At present, the key technology has not been mastered in the research and industrial application of ESC control in China, which makes the developed ESC products difficult to meet the needs of industrialization. This paper mainly studies the automotive engineering(AE) CS. Through the analysis of the ESC dynamics CS, we can understand the functional modules of the dynamics CS, and elaborate the control scheme of the ESC dynamics CS. We also study the vehicle yaw rate algorithm and analyze the vehicle yaw steering wheel angle and speed by chart analysis method. The experimental results show that the natural frequency of vehicle yaw is generally between 0.5Hz and 0.7hz. Compared with the single sinusoidal input test, the steering wheel angle required to excite the vehicle yaw rate response is smaller in the sinusoidal stagnation test. At 0.5Hz, the sinusoidal stagnation test needs to swing 130 degrees, while the single sinusoidal input test needs to swing 200 degrees.

New Adaptive Surrogate-Based Approach Combined Swarm Optimizer Assisted Less Tuning Cost of Dynamic Production-Inventory Control System

Today, most manufacturing control systems are complex and expensive, so they are limited to employ a small number of function evaluations for optimal design. Yet, looking for optimization methods with the less-computational cost is an open issue in engineering control systems. This paper aims to propose an effective adaptive optimization approach by integrating Kriging surrogate and Particle Swarm Optimization (PSO). In this method, a novel iterative adaptive approach is utilized using two sets of training samples including initial training and adaptive sample points. The initial training points are designed by space-filling design, while the adaptive points are generated using a new jackknife resampling approach. The proposed approach can effectively convergence towards the global optimal point using a small number of function evaluations. The efficiency and applicability of the proposed algorithm are evaluated using the optimal design of the fractional-order PID (FOPID) controller for some benchmark transfer functions. Then, the introduced approach is applied for tuning the parameters and the sensitivity analysis of the FOPID controller for a dynamic production-inventory control system. The results are in good agreement with the results reported in the literature, while the proposed approach is executed with a lower computational burden.

Dual Adaptive Model Predictive Control with Disturbances

Abstract Model-based control requires good accuracy of model parameters to achieve high performance. Controller design under parametric uncertainties is therefore a challenging topic in control system engineering. One well known control design for uncertain systems is adaptive control. An adaptive controller has two tasks: process regulation and parameter learning. Dual control explores the trade-off between the two seemingly conflicting tasks. The control structure in an adaptive system consists of a model-based controller and a recursive update rule for parameter estimation. In this paper, the adaptive control framework consists of model predictive control for system regulation and recursive least squares for parameter estimation. The requirement for persistent excitation is shown to be necessary for systems with high-dimensional parameter space. Then, a dual formulation is proposed in an attempt to generate excitation signals while maintaining control performance in the adaptive control scheme. The algorithm is implemented and tested on a simulated SISO system.

Relaxing Platform Dependencies in Agent-Based Control Systems

Agent-based systems have been widely used to develop industrial control systems when they are required to address issues such as flexibility, scalability and portability. The most common approach to develop such control systems is with agents embedded in a platform that provides software libraries and runtime services that ease the development process. These platforms also bring challenges to the agent-based control system engineering. For example, they might introduce default design features, such as a global directory of agents. Furthermore, agents are generally locked in a platform and depend on the platform’s available support for deployment across computing infrastructures. This article addresses these challenges through an approach for building agent-based control systems, that relaxes the dependencies in multiagent system (MAS) platforms, through the use of container-based virtualisation. The proposed approach is elaborated via a reference architecture that enables the implementation of agents as self-contained applications that can be deployed, on-demand, in independent environments but still are able to communicate and coordinate with other agents of the control system. We built a prototype using this approach and evaluated it in the context of a case study for the supervisory control of digital network infrastructures. This case study enabled us to demonstrate feasibility of the approach and to show the flexibility, of the resulting control system, to adopt several topologies as well as to operate at different scales, over emulated networks. We also concluded that designing agents as individual deployment units is also cost-effective especially in control scenarios with low number of stable agents.