Evolutionary Modeling of Industrial Plants and Design of PID Controllers

Pătraşcu, Monica; Ion, Andreea

doi:10.1007/978-3-319-26230-7_4

Cited by 20 publications

(15 citation statements)

References 40 publications

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“…This is due to the fact that a purely analytical approach in many cases does not provide a mathematical model that is sufficiently appropriate to the real control object. Therefore, the combined approach, when the general form of the mathematical description is determined analytically, and the values of the coefficients corresponding to a specific control object are determined experimentally is the most effective [1,13,17].…”

Section: Methodsmentioning

confidence: 99%

Implementation of the Methods of Identification of Static Control Objects

Pachkin¹,

Kotlyarov²

2017

Science Evolution

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One of the main tasks solved in the development of automatic control systems is the identification of the control object, which consists in obtaining its mathematical description. The nature and type of the mathematical model is determined by the goals and tasks for which it will be used. In the present case, the aim of obtaining the model is the synthesis of an automatic control system. Proceeding from the requirements of control problems, the identification problem consists in determining the structure and parameters of the mathematical model that ensure the best similarity of the model and object responses to the same input action. The article considers the experimental method of obtaining a mathematical description of the control object based on the results of measuring its input and output parameters and then processing the obtained results. The control object is the EP10 emulator made by the Oven Company, which is a miniature furnace. The emulator is used in experimental research in the process of commissioning using thermostat controls, and also applicable for educational purposes as part of training and research stands. As a result of structural identification with subsequent adjustment of the coefficients with the help of parametric identification, a model of the control object in the form of a second order aperiodic link is obtained. Parameters and type of the mathematical model allowed to make calculations and determine the parameters of adjustment of the TRM251 PID-controller. The software implementation of the automatic control system in the MatLAB environment made it possible to evaluate transient processes in a closed system. Thus, the calculation and analysis of the automatic control system in the first approximation were made. The final result can be obtained at the stage of commissioning the automatic temperature control system in the EP10 emulator using adaptation algorithms.

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Section: Methodsmentioning

confidence: 99%

Implementation of the Methods of Identification of Static Control Objects

Pachkin¹,

Kotlyarov²

2017

Science Evolution

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“…To investigate the frequency control based on the optimal values of the fuzzy controller parameters, the Genetic Algorithm Optimization Toolbox (GAOT) algorithm is used [45]. Fuzzy, adaptive, and allied controllers are vastly investigated for various control purposes in industries and power systems [46]- [50].…”

Section: ) Adaptive Droop Controlmentioning

confidence: 99%

Adaptive and Fuzzy PI Controllers Design for Frequency Regulation of Isolated Microgrid Integrated With Electric Vehicles

et al. 2020

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The number of electric vehicles and renewable energy resources integrated into the power system is increasing day by day. The objective behind the development of electric vehicles and renewable energy sources is to build a sustainable and green power system. The renewables either don't possess system inertia or have less system inertia, therefore, they don't effectively respond to the load variations. The battery storage system of electric vehicles is used as the first line of defense to counter the effect of load/frequency variations and make the system stable. As active power is inversely proportional to the system frequency, for this purpose electric vehicles are included in the microgrid environment. In this paper, an isolated microgrid having a reheat turbine system, wind turbine system, photovoltaic system, and electric vehicles is studied. The output of the renewables is not controlled to utilize its maximum output power. Therefore, adaptive droop control and fuzzy PI control mechanisms are implemented to cater to the frequency variations of the isolated microgrid; the former regulates the power of electric vehicles while maintaining the energy needs of each EV and the later controls the output power of reheat turbine system according to the frequency variation. Furthermore, the genetic algorithm optimization toolbox is utilized to optimize the parameters of the adaptive and fuzzy PI controllers. The proposed model is developed in MATLAB/Simulink which shows that these control techniques effectively sustained the system frequency of isolated microgrid in the desired limits. INDEX TERMS Adaptive droop control, electric vehicles, frequency regulation, fuzzy PI control, GA optimization technique, renewable energy sources, reheat turbine system.

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“…In the crossover operation, a certain part of individuals is selected and swap in a way to get the good part of from old one. Next stage is mutation, and it is random alteration of a value of a string position in the individuals [Patrascu and Ion, 2016]. The major stages of the GA, are performed as per following steps for selecting the optimal parameters:…”

Section: Simulation Experiments For Haptic Interfacementioning

confidence: 99%

“…To obtain the best fit optimal solution under the constraints various optimization techniques such as Ziegler Nichols (Z-N) method, Neural Network (NN) [Grubov et al, 2017;Kumar and Ohri, 2018], Fuzzy Logic Con-troller (FLC) [Kumar and Ohri, 2017], Genetic Algorithm (GA) and Particle Swarm Optimization (PSO) [Patrascu and Ion, 2016] are available in literature. These techniques have their own advantages and disadvantages, which varies from system to system.…”

Section: Introductionmentioning

confidence: 99%

“…Fuzzy Logic Controller (FLC) shown the remarkable result in the various uncertain environment over different system configurations [Dehghani and Khodadadi, 2015] but it requires ample knowledge base of the system. GA uses the process of natural selection and provides the optimal solution among all potential solutions for a particular problem [Patrascu and Ion, 2016], but sometimes it gets stuck in local minimum and longer time is taken for convergence. PSO is another search optimization technique, based upon swarm and offer a better solution in terms of convergence time and global search space [Kennedy, 1997].…”

Section: Introductionmentioning

confidence: 99%

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Optimal interface controller design for haptic system

Kumar

Ohri

2020

CAP

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Haptic technology is the future oriented technology having wide applications in various field from medical, military and pilot training to video games and smartphones. In medical, it can prove a major asset for training the surgeon. The key performance issues in a haptic system are stability and transparency. This paper presents the design of haptic interface controller (HIC) for a 1-DOF haptic system designed to be used by surgical practitioners. The designed controller emphasizes on stability and transparency under the presence of uncertainty and delay. The optimal parameter of HIC has been selected using Genetic Algorithm (GA) and modified Particle Swarm Optimization (m-PSO). The performance of the designed controller is evaluated in terms of performance measure and compared with the conventional tuning method.

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Evolutionary Modeling of Industrial Plants and Design of PID Controllers

Cited by 20 publications

References 40 publications

Implementation of the Methods of Identification of Static Control Objects

Implementation of the Methods of Identification of Static Control Objects

Adaptive and Fuzzy PI Controllers Design for Frequency Regulation of Isolated Microgrid Integrated With Electric Vehicles

Optimal interface controller design for haptic system

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