Optimization Of Control System Research Articles

Optimization of Robust LMI-Control Systems for Unstable Vertical Plasma Position in D-Shaped Tokamak

The paper is devoted to the synthesis, comparison, and optimization of robust LMI-control systems for the vertical plasma position in a D-shaped tokamak, specifically the T-15MD tokamak (Kurchatov Institute, Moscow, Russia). The novelty of this work is to find out the possibilities of LMI robust control systems, according to the criteria of the robust stability radius and control power peak at the rejection of a minor disruption type disturbance and a reference step signal using a unique unstable first-order plasma model. The plant under control consists of the connected in series plasma model with additive disturbance containing plant uncertainties, horizontal field coil (HFC), and actuator model as a multiphase rectifier. A set of robust controllers was designed by Linear Matrix Inequalities (LMI) method with pole placement in the LMI regions, state H2/H∞ performance, and output signal performance. The LMI theorems of the paper are directed to design the robust controllers and study the systems with the aim of eliminating the gap between theory and practice. The main achievement of this work consists in the optimization of robust control systems of the unstable plant with uncertain disturbance on the set of LMI synthesis approaches. The control systems have original quality criteria, such as control power and robust stability radius. The best control system on the basis of two criteria, namely, Dα,r,ϑ control system provides stabilization of the vertical plasma position on the real-time digital control testbed.

Asistensi Penyusunan SBI e-Pocketbook sebagai Alat Pengendalian Anggaran Berbasis Kinerja

<p class="8Abstractcontent"><span lang="EN-US">As one of the educational institutions under the Ministry of Religion Indonesia established in 2016, STAIN Majene is led to conduct the optimization of a performance-based budgetary control system through costing standards. This development activity was designed using Participatory Action Research (PAR). The model of development of the product used 4D model (</span><span lang="EN-US">Define, Design, Development, and Dissemination) adopted from the model of Thiagarajan et al. (1974). Afterward, users of budgeting were taught so they were proficient at using the product of Internal Cost Standard (SBI). </span><span lang="EN-US">The result of this activity discloses that the SBI book is successful to be produced in the form of a digital known as the SBI e-pocketbook to budgetary users in STAIN Majene. It is a costing unit acting as the guidance of internally budgetary calculation of STAIN Majene and a necessary instrument in making budgetary allocations so that SBI product contributes to budgetary users in STAIN Majene. </span></p>

Physics-informed optimization of robust control system to enhance power efficiency of renewable energy: Application to wind turbine

This study mainly focused on wind turbine (WT) design in urban environments because the negative torque generated by the returning wind blade profile was the primary cause of its low power efficiency. The turbine chosen in this study was a vertical-axis wind turbine (H-VAWT). However, the physics-informed optimization of the robust control system (RCS) design of the WT was challenging because the rotor blades (NACA 0012 airfoil) had nonlinear aerodynamic characteristics. Furthermore, our proposed controller was used in this study because of its ability to suppress disturbances and ensure stability against various uncertainties. The electrical block setup used an H-type VAWT based on advanced direct vector control generators. This indicated that a low-inertia turbine was more acceptable than a high-inertia turbine because of its suitability for maximum power conversion. Overall, there was a substantial active power gain of approximately 25% greater than conventional control systems.

Machine learning models in people detection and identification

Introduction: This article is the result of research entitled "Development of a prototype to optimize access conditions to the SENA-Pescadero using artificial intelligence and open-source tools", developed at the Servicio Nacional de Aprendizaje in 2020. Problem: How to identify Machine Learning Techniques applied to computer vision processes through a literature review? Objective: Determine the application, as well as advantages and disadvantages of machine learning techniques focused on the detection and identification of people. Methodology: Systematic literature review in 4 high-impact bibliographic and scientific databases, using search filters and information selection criteria. Results: Machine Learning techniques defined as Principal Component Analysis, Weak Label Regularized Local Coordinate Coding, Support Vector Machines, Haar Cascade Classifiers and EigenFaces and FisherFaces, as well as their applicability in detection and identification processes. Conclusion: The research led to the identification of the main computational intelligence techniques based on machine learning, applied to the detection and identification of people. Their influence was shown in several application cases, but most of them were focused on the implementation and optimization of access control systems, or tasks in which the identification of people was required for the execution of processes. Originality: Through this research, we studied and defined the main machine learning techniques currently used for the detection and identification of people. Limitations: The systematic review is limited to information available in the 4 databases consulted, and the amount of information is variable as articles are deposited in the databases.

Leveraging the benefits of ethanol-fueled advanced combustion and supervisory control optimization in hybrid biofuel-electric vehicles

The advanced combustion-based dedicated hybrid engines (DHEs) operating with biofuels demonstrate great advantages to reduce greenhouse gas emissions. To explore the potential of this solution, the synergistic effect of bio-ethanol and spark-induced compression ignition (SICI) combustion with reliance on efficient supervisory control systems is evaluated for greenhouse gas (GHG) emission reduction. This article is pioneered with the forward engineering of SICI combustion engine for plug-in hybrid biofuel-electric vehicles (PHBEVs) by optimizing the high-efficiency region of SICI combustion system for the PHBEV with dynamic programming (DP)-based case studies, after which the DHE prototype is produced using one-dimension and three-dimension engine digital twin models. Further, experimental test data of the optimized DHE is used to model the PHBEV in GT-Suite and MATLAB/Simulink software, and its charge-sustaining (CS) performance with rule-based control and adaptive equivalent minimization control strategy (A-ECMS) under both homologation and real-world driving cycles is evaluated and compared with the offline DP. The results show that the SICI combustion engine-based PHBEV with ethanol blending from E20 to E100 can reduce the well-to-wheel (WTW) CO2 emissions by 28% to 75%, respectively, where more than 7% of the reduction is contributed by control system optimization using A-ECMS. Moreover, the digital twin-based simulation platform developed in this work can be applied to evaluate and optimize the advanced combustion engines and supervisory controllers for hybrid biofuel-electric vehicles.

Optimasi PID Kontroller Pada Sistem Pengaturan Irigasi Menggunakan Metode Bat Algorithm (BA)

Provision of irrigation water on agricultural land aims to meet crop water needs. In its utilization, irrigation water must be used optimally. An automatic irrigation system is needed that is able to provide water for plants with the expected conditions. PID (Proportional, Integral and Derivative) is widely used for optimization of control systems. In irrigation control system and sensor connection with software program (Matlab 2013b). PID simulation was applied to the irrigation prototype used to increase efficiency and determine the amount of irrigation water to regulate the provision of irrigation water as needed. As a comparison controller, conventional PID (PID-Konv) was used, Matlab auto tuning PID (PID-Auto), and Bat Algorithm tuning PID (PID-BA). The simulation results show that PID-BA is the best method in the simulation with the smallest overshot (0.043) and the fastest settlingtime (84 seconds).

Construction of Early Warning Model for Financial Economic Cycle Using Genetic Algorithm

Economic crises occur when the economy experiences drastic fluctuations, which in turn cause heavy losses to society and the economy. Economic crises happen when there are significant losses to society and the economy as a result of the economy experiencing abrupt changes or deep recessions. The term “economic cycle early warning mechanism” refers to a collection of theories and techniques for keeping track of, assessing, forecasting, and choosing policies regarding finances in light of the particular economic phenomenon known as economic cycle fluctuations. The traditional early warning mechanism has been unable to keep up with the demands of economic cycle early warning due to the growth of economic globalisation, and the phenomenon of incomplete early warning and poor early warning accuracy frequently occurs. A genetic algorithm simulates the natural selection and genetic mechanisms of Darwin’s theory of biological evolution. It is a computational model of the biological evolution process. It is a technique for looking for the best answer by simulating the course of natural evolution. Genetic algorithms have advanced incredibly quickly in recent years. It has been widely applied in machine learning, neural networks, control system optimization, and the social sciences as an effective, useful, and reliable optimization technique. In order to optimize the early warning mechanism and increase the thoroughness and accuracy of early warning, this paper investigates the early warning mechanism of financial and economic cycles by combining genetic algorithms. A mathematical model of the economic cycle was built during the experiment and tested in accordance with the genetic algorithm’s basic operating principle. The findings demonstrate that the genetic algorithm-based early warning system for the financial economic cycle is more complete and accurate, with a 6.4% increase in accuracy.

Optimization method of the thermal control index of space remote sensors based on force-thermal coupling algorithm

To ensure the successful completion of space observation tasks, the temperatures of optical remote sensors must be maintained within appropriate ranges using thermal control systems to avoid the influence of the space thermal environment on imaging performance. In engineering, an appropriate temperature range must be accurately predicted for a remote sensor in orbit through a reasonable thermal control index to guide the design and optimization of the thermal control system. This study analyzes the environmental differences between systems in space and on Earth, calculates the temperature requirements of the imaging performance index as a constraint, investigates the coupling influence mechanism of gravity release and temperature change on system imaging performance, and proposes a method to optimize the thermal control index of a remote sensor. The proposed method can optimize the thermal control design to obtain sufficientimaging performance in orbit, reduce the thermal control power consumption by approximately 20%, and shorten the development cycle; it is also suitable for various types of remote sensors. This study verifies the rationality of the method through a combination of an orbital thermal simulation and a thermal vacuum test.

Judgment of Social Governance System Risk Based on Big Data Analysis

Accurate risk identification, scientific risk assessment, effective risk early warning, and real-time risk monitoring will all benefit from the advancement of data science and the development of the data industry. Simultaneously, social risk prevention and control face numerous obstacles in the growth of information technology, data integration and mining, data information disclosure, and data culture popularization in the era of big data. It can be improved in terms of risk prevention and control concept innovation. In addition, it can also be improved in terms of network public opinion guidance, control mechanism improvement, social security prevention, control system optimization, and the introduction and training of big data talent to promote scientific and accurate social risk prevention and control. As society moves into the 5.0 era, many social risks rise and new features emerge, posing larger threats and unprecedented challenges to social risk governance. Big data, as a result of the emerging technology revolution, offers a wealth of governance resources and brand-new governance concepts that traditional governance mechanisms lack, offering technical assistance. This article presents a parallel community partition algorithm using MapReduce to analyze big data. The proposed model is established for the big data thinking paradigm to promote social risk governance innovation. The proposed model supports the top-level model and does inclusive forecasting for social risk management that supports resource integration. The proposed model also realizes diversified and coordinated risk governance. The results reveal the significance of the proposed model.

Smart Urban Mobility: When Mobility Systems Meet Smart Data

Cities around the world are expanding dramatically, with urban population growth reaching nearly 2.5 billion people in urban areas and road traffic growth exceeding 1.2 billion cars by 2050. The economic contribution of the transport sector represents 5% of the GDP in Europe and costs an average of US <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\$ $ </tex-math></inline-formula> 482.05 billion in the United States. These figures indicate the rapid rise of industrial cities and the urgent need to move from traditional cities to smart cities. This article provides a survey of different approaches and technologies such as intelligent transportation systems (ITS) that leverage communication technologies to help maintain road users safe while driving, as well as support autonomous mobility through the optimization of control systems. The role of ITS is strengthened when combined with accurate artificial intelligence models that are built to optimize urban planning, analyze crowd behavior and predict traffic conditions. AI-driven ITS is becoming possible thanks to the existence of a large volume of mobility data generated by billions of users through their use of new technologies and online social media. The optimization of urban planning enhances vehicle routing capabilities and solves traffic congestion problems, as discussed in this paper. From an ecological perspective, we discuss the measures and incentives provided to foster the use of mobility systems. We also underline the role of the political will in promoting open data in the transport sector, considered as an essential ingredient for developing technological solutions necessary for cities to become healthier and more sustainable.

ელექტრული ღუმლის ელექტროდების გადაადგილების მართვის სისტემის ოპტიმიზაცია

It’s known that the efficient operation of electrical appliances used in production significantly improves the quality and quantity of products. Competent control of technological processes is especially important for such a heavy industry as metallurgical production, where one of the most important issues is the optimal control system for electric motors of moving electrodes, which in turn ensures the required dynamics of arc power control and high productivity. The existing arc power control systems do not fully meet the basic requirements, such as insufficient sensitivity of the regulating parameter, speed and reliability of the electrode movement motor control system. The study of electric motor control systems is an important scientific and technical task in this regard. The optimization of the electric motor control system for moving the electrodes of an electric arc furnace based on the use of logic in control is discussed.

Методика оптимизации системы автоматического регулирования методом направленного поиска

This research is dedicated to automatic control system optimization using a directional search procedure. Simulation was conducted in MATLAB by using frequency response and the theory of random processes. When we know a value of noise power, we can tell the standard deviation of the controlled variable which is necessary to determine if system’s accuracy is good.

Идентификация процессов судового оборудования методами интроспективного анализа

For introspective identification of the automatic control system (ACS) of a DC motor there has been developed a specific simplified technique of studying the optimization of a linear automatic control system. The technique is based on using the MATLAB computer simulation program, which helps to easily obtain the transition function of the ACS model, on the understanding a few ACS properties and using the directed search method. The work can be done in three stages, in accordance with the tasks. Draft models are considered, in which the researcher must set up the parameters of the elements, activate the simulation process and keeping under control the type of the transient function select the values of the optimal gains of the controllers. The time constant of the PI controller is chosen equal to the largest time constant of the ACS loop elements. It is often the time constant of the control object. In this case it is the time constant of a DC motor. The optimal choice of the system will also determine the tasks of identification, due to which it will be possible to correctly select a technological object by a specific ACS, its control variable and disturbance to provide the solution of the problem facing ACS. The static characteristics of an object determine the dependence of the controlled variable on the controlling variable and disturbances. In simple cases they can be obtained analytically by using the physical laws that link the controlled variable to the input parameters. Parametric optimization of indicators has been identified and tested on simulation examples in a simulation package and makes it possible to judge the correctness of simulation with a minimum error value.

Model Optimization of Agricultural Machinery Information Control System Based on Artificial Intelligence

Agricultural mechanization information in our country has the main problems existing in the management and utilization. The analysis of China’s agricultural mechanization management model and related software is presented based on combining modern science and technology as well as the development of agricultural mechanization management information system based on network software to standardize the management information collection, processing, storage and transmission, agricultural mechanization management information science, standardization, automation, etc. According to the analysis, the output target speed after fusion is more stable, and the stability is increased by 59.59% compared with the single-point GNSS velocity measurement data, and by 18.32% compared with the data measured by the binocular vision velocity measurement system. It has realized the goal of accurate speed measurement from low speed to high speed. In particular, it has solved the problems such as vehicles unable to complete positioning and vehicle skidding caused by trees blocking GNSS satellite signals during field operations.

Time-delay feedback control of a cantilever beam with concentrated mass based on the homotopy analysis method

In this paper, the strongly nonlinear vibration of a cantilever beam system with concentrated mass at an intermediate position controlled by displacement and velocity time delay is investigated. The nonlinear governing equation is studied using the homotopy analysis method. The effects of the time delay, displacement and velocity feedback gain coefficients, as well as frequency on the amplitude of the system were studied in detail. The results indicate that the velocity feedback control is not necessarily superior to displacement feedback control. Reasonable selection of the displacement and velocity time delay parameters can avoid the Hopf bifurcation, adjust the number, amplitude and stability of periodic solutions, and exhibit the softening behavior at low frequency and hardening spring characteristic at high frequency. The theoretical research in this paper will promote the application of homotopy analysis method in the field of time delay control, and serve as a theoretical reference for the design and optimization of cantilever beam control systems with concentrated mass.

HYBRID MULTI-AGENT METHOD FOR OPTIMIZATION OF FUZZY COMPUTER SYSTEMS

Recently, intelligent computer systems based on fuzzy logic and soft computing are used quite effectively to solve a wide range of complex applied problems in various fields of human activity. One of the promising areas of modern research in the field of artificial intelligence is the creation and testing of bioinspired multi-agent and evolutionary methods for the synthesis and optimization of fuzzy automatic control systems (ACS) and decision support systems (DSS). This paper is devoted to the development and research of a multi-agent method of parametric optimization of fuzzy systems (FS) based on hybrid improved grey wolf algorithms. The proposed method allows optimizing the parameters of fuzzy computer systems more efficiently, compared to the basic and improved grey wolf methods. This method uses group hunting and dimensional learning-based hunting strategies, as well as local search strategies based on algorithms of gradient descent and extended Kalman filter, which significantly reduces computational costs and increases the rate of convergence to optimal solutions when optimizing parameters of complex fuzzy systems. To study the effectiveness of the developed hybrid multi-agent method in this work, the synthesis and parametric optimization of the fuzzy flight control system for the unmanned aerial vehicle (UAV) is carried out. In particular, the optimization of the vectors of normalizing coefficients, adjustable parameters of linguistic terms (LT), and weight coefficients of the rule's consequences of the rule base (RB) for the altitude controller of the fuzzy ACS for the UAV is performed. The obtained results of the comparative analysis confirm the significant advantages of the developed hybrid multi-agent method of parametric optimization, as well as the feasibility of its application for the synthesis of different types of ACSs and DSSs based on fuzzy logic.

Thermodynamic Optimization of Aircraft Environmental Control System Using Modified Genetic Algorithm

This paper presents an optimization method for the civil aircraft environmental control system (ECS) mainly involving two airstreams: the ram airstream for cooling and the bleed airstream for supplying the cabin. The minimum total fuel energy consumption rate (FECR), defined as the weighted sum of the shaft power extraction and propulsive power loss, is obtained under the precondition of the constant outputs in the cooling capacity and outlet pressure. A modified genetic algorithm (GA) is proposed to acquire the optimal values of the heat transfer areas, temperature ratio of bleed air, mass flow rate of ram air, and pressure ratios of the turbine, compressor, and fan. The statistical results show that the multipoint crossover and continuity improvement implemented in the modified GA improve convergence and distribution performance. The probability of reaching a satisfactory result using modified GA is 62.4% higher than standard GA. Due to the decrease of inlet parameters of bleed air and the elimination of power input in the compressor, the FECR of the optimization case can be lowered by 11.0%. In general, the evaluation method considering energy quality together with the modified optimization technique is proved effective in energy-saving design for such energy systems such as ECS with multiple inputs and outputs.

Optimization of environment control system for narrow sleeping space in underground shelters

As one of the main functions of urban underground spaces, underground shelter can be utilized for prevention/mitigation of disasters. In underground shelter, narrow sleeping spaces are recommended due to cost-effective and space-saving charcteristics. However, it is challenging to create a satisfied indoor environment with narrow sleeping spaces because of the limited space, high density of occupants and complex physical fields. In this study, an environmental control system was proposed for narrow sleeping spaces with multiple occupants, and numerical simulations were adopted in design and optimization process. Firstly, a numerical strategy (including turbulence modeling, meshing method and experimental validation) was proposed for the environment control system. Secondly, the validated numerical model was adopted to investigate the influences of key design parameters (such as inlet air velocity, temperature, air supply angle and airflow pattern) on indoor environment and energy consumption of narrow sleeping spaces with airflow pattern of “Up-supply and Down-return”. Thirdly, the CFD-GA (Genetic Algorithm) method was utilized to optimize multiple design parameters of the environment control system. The optimal results indicated that all of the indexes (predicted percentage dissatisfied [PPD], percentage dissatisfied of draft sensation [PD] and carbon dioxide [CO2] concentration) satisfied the requirement of standards, and its energy consumption was much lower than other design conditions. Compared to the “Up-supply and Down-return” airflow pattern, the “Down-supply and Up-return” airflow pattern produced higher PD values and caused stronger draft sensation. Overall, the numerical strategy and CFD-GA optimization method is able to provide proper design of indoor environment control for the underground narrow sleeping space, from the perspectives of thermal comfort, air quality and energy saving.

Optimization of automatic power control of pulsed reactor IBR-2M in the presence of instability

The paper presents the main results of computational and experimental optimization of the automatic power control system (AС) of the IBR-2M pulsed reactor in the presence of a high level of oscillatory instability. Optimization of the parameters of the AС made it possible to significantly reduce the influence of random and deterministic oscillations of reactivity on the noise of the pulse energy, as well as to sharply reduce the manifestation of the oscillatory instability of the reactor. As a result, the safety and reliability of operation of the reactor has increased substantially.

Fire System Analysis and Optimization of Suitable Fire Control System

Fire is one of the major hazards which may occur due to natural or man-made causes. This report presents the importance of suitable fire systems in case of fire. In general, fires may be classified into five categories depending on the fuel that is burning. In recent times, there is an increase in incidence of fires in urban population due to very densely populated areas. The losses associated due to fires can be classified as loss to the life of structure, loss of human life and loss to property. Analysis of fire loads of different compartments and providing sufficient number of firefighting equipment’s can help in reducing the severity of fires. The fire loads and fire densities were analyzed at some locations to determine the requirements of firefighting equipment’s with suitable extinguishing agents. Fire risk assessment is the process of identifying the hazards present in the various places and the people who are at risk and evaluate and reduce the risk by discuss with the employees and taking corrective actions. The assessment activities are record and documented for the future purpose. This report includes study of various types of fire, fire extinguishers, calculation of fire load and selection of suitable fire system to reduce and control the fire accidents as per legal standards.