Fuzzy Multi-Regional Fractional PID controller for Pressurized Water nuclear Reactor

Puchalski, Bartosz; Duzinkiewicz, Kazimierz

doi:10.1016/j.isatra.2020.04.003

Cited by 32 publications

(7 citation statements)

References 26 publications

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“…This process is valid when one solution is assigned to the intersection of the plots Mendez et al [37] presented several algorithms of automatic PID controller tuning using implicit and explicit identification algorithms. Modern tuning methods include those utilizing: genetic algorithms [50,51,55], fuzzy models [48,49] and artificial neural networks [29].…”

Section: Fig 1 Automatic Control System Making Use Of a Pid Controllermentioning

confidence: 99%

Comparable analysis of PID controller settings in order to ensure reliable operation of active foil bearings

Witanowski¹,

Breńkacz²,

Szewczuk-Krypa³

et al. 2022

Eksploatacja i Niezawodność – Maintenance and Reliability

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In comparison to the traditional solutions, active bearings offer great operating flexibility, ensure better operating conditions over a wider range of rotational speeds and are safe to use. In order to ensure optimum bearing performance a bearing control system is used that adapts different geometries during device operation. The selection of optimal controller parameters requires the use of modern optimization methods that make it possible to quickly achieve the assumed parameters. This article presents the method that has been employed to select the parameters of a proportional integral derivative (PID) controller, in which both stochastic algorithms and hybrid methods have been compared. The results show that all of the used algorithms were able to reach the global optimum but only the hybrid algorithm was repeatable in all runs within a low value of the standard deviation. The best solution will be proposed in the future to control an active foil bearing. Analysing of this paper would help to prevent failures of active foil bearing used in the designed rotating machine.

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Section: Fig 1 Automatic Control System Making Use Of a Pid Controllermentioning

confidence: 99%

Comparable analysis of PID controller settings in order to ensure reliable operation of active foil bearings

Witanowski¹,

Breńkacz²,

Szewczuk-Krypa³

et al. 2022

Eksploatacja i Niezawodność – Maintenance and Reliability

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“…Duzinkiewicz, [12] proposed a fuzzy fractional order PID controller to control the average thermal power of a nuclear reactor. In this work, the performance indices like ISE, IAE, ITAE of the FMR FOPID are compared with FOPID and conventional PID controller for robustness and smooth control signal.…”

Section: Introductionmentioning

confidence: 99%

pH control in sodium chlorate cell for energy efficiency using PSO-FOPID controller

Sreekumar

Kallingal

Lakshmanan

2022

CI&CEQ

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Industrial sodium chlorate production is a highly energy-intensive electro-chemical process. If the pH of the chlorate cell is not controlled, the current efficiency drops from 99% to as low as 66.66%. Hence control of chlorate cell pH is very significant for energy-efficient sodium chlorate production. This study puts forward a fractional order PID controller for controlling the pH of the sodium chlorate cell. The tuning of FOPID controller variables is affected by employing particle swarm optimization. The highlight of the controller is that it is flexible, easy to deploy and the time of computation is significantly low as few parameters are needed to be adjusted in PSO. The performance analysis of the suggested FOPID-PSO controller was studied and compared with the traditional PI controller and PID controller using time-domain provisions like settling time, rise time and peak overshoot and error indicators like integral square error (ISE), integral absolute error (IAE), and integral time absolute error (ITAE). FOPID controller employing PSO proved to perform well compared to conventional controllers with 0.5 sec settling time and 0.1 sec rise time. This demonstrates that the FOPID-PSO controller has better setpoint tracking, which is very essential for the process under consideration.

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“…In the last few decades, the interest in fractional-order calculus has grown in a very significant way in both the research and engineering fields, with special attention in modeling and simulation of physical phenomena as reported in [2] and [3]. Applications of the fractional order calculus can be found in areas such as astronomy [4], finances [5], time delay systems studies [6], fuzzy inference systems [7], nonlinear chaotic systems [8], unmanned aerial and ground vehicles [9], modeling of electrochemical capacitors [10], modeling and control of nuclear reactors [11], [12], and many others. Fractional operators…”

Section: Introductionmentioning

confidence: 99%

“…The main reason and at the same time the fundamental disadvantage is that definition based fractional operators need an infinite history of signal samples based on which the output signals from, i.e. fractional controllers [12] or fractional observers [31], [32], etc. are evaluated.…”

Section: Introductionmentioning

confidence: 99%

Neural Approximators for Variable-Order Fractional Calculus Operators (VO-FC)

Puchalski

2022

IEEE Access

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The paper presents research on the approximation of variable-order fractional operators by recurrent neural networks. The research focuses on two basic variable-order fractional operators, i.e., integrator and differentiator. The study includes variations of the order of each fractional operator. The recurrent neural network architecture based on GRU (Gated Recurrent Unit) cells functioned as a neural approximation for selected fractional operators. The paper investigates the impact of the number of neurons in the hidden layer, treated as a hyperparameter, on the quality of modeling error. Training of the established recurrent neural network was performed on synthetic data sets. Data for training was prepared based on the modified Grünwald-Letnikov definition of variable-order fractional operators suitable for convenient numerical computing without memory effects. The research presented in this paper showed that recurrent network architecture based on GRU-type cells can satisfactorily approximate targeted simple yet functional variable-order fractional operators with minor modeling errors. In addition, the research also compares the presented solution with basic and recurrent neural networks that utilize Tapped Delay Lines (TDL) in their structure. The presented solution is a novel approach to the approximation of VO-FC operators. It has the advantage of automatic selection of neural approximator parameters by optimization based on data customized for specific requirements.

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Fuzzy Multi-Regional Fractional PID controller for Pressurized Water nuclear Reactor

Cited by 32 publications

References 26 publications

Comparable analysis of PID controller settings in order to ensure reliable operation of active foil bearings

Comparable analysis of PID controller settings in order to ensure reliable operation of active foil bearings

pH control in sodium chlorate cell for energy efficiency using PSO-FOPID controller

Neural Approximators for Variable-Order Fractional Calculus Operators (VO-FC)

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