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Journal articles on the topic 'PI Tuning'

Author: Grafiati

Published: 4 June 2025

Last updated: 1 August 2025

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1

Arauz, Teresa, José M. Maestre, Xin Tian, and Guanghua Guan. "Design of PI Controllers for Irrigation Canals Based on Linear Matrix Inequalities." Water 12, no. 3 (2020): 855. http://dx.doi.org/10.3390/w12030855.

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A new Proportional-Integral (PI) tuning method based on Linear Matrix Inequalities (LMIs) is presented. In particular, an LMI-based optimal control problem is solved to obtain a sparse feedback that provides the PI tuning. The ASCE Test Canal 1 is used as a case study. Using a linearised model of the canal, different tunings for the design of the PI controller are developed and tested using the software Sobek. Furthermore, the proposed method is also compared with other tunings proposed for the same canal available in the literature. Our results show that the proposed method reduces by half th

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2

Boiko, Igor. "SELF-TUNING PI CONTROLLER." IFAC Proceedings Volumes 39, no. 7 (2006): 29–34. http://dx.doi.org/10.3182/20060625-4-ca-2906.00011.

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3

Vítečková, Miluše, and Antonín Víteček. "PI and PID Controllers Tuning." IFAC Proceedings Volumes 33, no. 13 (2000): 101–5. http://dx.doi.org/10.1016/s1474-6670(17)37173-2.

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4

Wang, Ya-Gang, and Hui-He Shao. "Optimal tuning for PI controller." Automatica 36, no. 1 (2000): 147–52. http://dx.doi.org/10.1016/s0005-1098(99)00130-2.

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5

Klàn, P., and R. Gorez. "Balanced Tuning of PI Controllers." European Journal of Control 6, no. 6 (2000): 541–50. http://dx.doi.org/10.1016/s0947-3580(00)71117-4.

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6

Airikka, Pasi. "Robust Predictive PI Controller Tuning." IFAC Proceedings Volumes 47, no. 3 (2014): 9301–6. http://dx.doi.org/10.3182/20140824-6-za-1003.00958.

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7

Wang, Qing-Guo, and He Ru. "Pi Tuning Under Performance Constraints." Asian Journal of Control 4, no. 4 (2008): 397–402. http://dx.doi.org/10.1111/j.1934-6093.2002.tb00079.x.

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8

Bhuvanendhiran, T. "Performance Evaluation of Nonlinear PI Controller on the Laboratory Type Spherical Tank Process." Asian Journal of Electrical Sciences 8, no. 2 (2019): 16–20. http://dx.doi.org/10.51983/ajes-2019.8.2.2364.

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In this paper, the implementation of Nonlinear PI controller based on error square type is designed and adopted to control of level in a spherical tank process. By use of black box model the system is found as a First order plus Dead Time model (FOPDT). Then the controller tuning strategies has been adopted namely Direct synthesis (IMC), Skogestad (IMC PI), and Nonlinear PI (error square type) tuning. Among all the three controllers tuning the error square type based Nonlinear PI tuning method shows better control performance than the other two controller tuning in terms of performance indices

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9

Gude, Juan J., and Evaristo Kahoraho. "New tuning rules for PI and fractional PI controllers." IFAC Proceedings Volumes 42, no. 11 (2009): 768–73. http://dx.doi.org/10.3182/20090712-4-tr-2008.00125.

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10

Li, Guang Zhong. "Application of Fuzzy-PI Controller in the Asynchronous Motor." Advanced Materials Research 1070-1072 (December 2014): 1210–15. http://dx.doi.org/10.4028/www.scientific.net/amr.1070-1072.1210.

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This paper proposes an induction motor variable frequency speed control system based on fuzzy-PI self-tuning. The system includes coordinate transformation modules, vector control modules, space vector pulse width modules, fuzzy-PI self-tuning speed regulator, excitation current and torque current PI regulator. Because the speed regulator controlled by the fuzzy-PI self-tuning has better control effect than traditional PI. The performance can be reflected by the shorter response time, smaller overshoot. In addition, when the load torque changes, the electromagnetic torque changes more smoothly

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11

Ji, Ya Feng, Dian Hua Zhang, Jie Sun, and Xu Li. "Smith Prediction Monitor AGC System Based on CPSO Self-Tuning Pi Control." Advanced Materials Research 753-755 (August 2013): 2602–6. http://dx.doi.org/10.4028/www.scientific.net/amr.753-755.2602.

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In accordance with the feature of pure delay in monitor AGC system for hot rolling mill, a new self-tuning PI Smith prediction controller based on chaotic particle swarm optimization (CPSO) is developed. The algorithmic principle and design method of new controller are given. Based on the typical monitor AGC model of tandem hot mill, the analysis of dynamic performance for traditional PI Smith prediction controller and CPSO self-tuning PI Smith prediction controller is done by MATLAB. The simulation results indicate that CPSO self-tuning PI Smith prediction controller has faster response and h

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12

Li, Shuang Shuang, and Yong Xin Liu. "AC Motor Speed Control Double Close Loop Cascade Fuzzy PI Controller Design." Advanced Materials Research 516-517 (May 2012): 1571–74. http://dx.doi.org/10.4028/www.scientific.net/amr.516-517.1571.

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Cascade Speed Regulation System has the nonlinear characteristics and whose structure parameters are variable easily. Fuzzy control theory which based on conventional Cascade Speed Regulation System is introduced into such system. An asynchronous motor model is built, and a fuzzy parameter self-tuning controller is designed in this paper, the controller is simulated in Matlab, The simulation result shows the difference between the conventional PI controller and the fuzzy parameter self-tuning PI controller. From these simulation results, all performance indexes of the fuzzy PI controller with

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13

Viteckova, M., and A. Vitecek. "2DOF PI and PID Controllers Tuning." IFAC Proceedings Volumes 43, no. 2 (2010): 343–48. http://dx.doi.org/10.3182/20100607-3-cz-4010.00061.

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14

Ganchev, Ivan, Michail Petrov, Katerina Hyniova, and Antonin Stribrsky. "Auto-Tuning of Predictive PI Controller." IFAC Proceedings Volumes 33, no. 14 (2000): 769–74. http://dx.doi.org/10.1016/s1474-6670(17)36323-1.

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15

Menani, Smail, and Heikki N. Koivo. "Relay Tuning of Multivariable PI Controllers." IFAC Proceedings Volumes 29, no. 1 (1996): 5078–83. http://dx.doi.org/10.1016/s1474-6670(17)58486-4.

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16

Mudi, Rajani K., and Nikhil R. Pal. "A self-tuning fuzzy PI controller." Fuzzy Sets and Systems 115, no. 2 (2000): 327–38. http://dx.doi.org/10.1016/s0165-0114(98)00147-x.

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17

Devanathan, R. "Expert Self-Tuning PI(D) Controller." IFAC Proceedings Volumes 24, no. 1 (1991): 217–22. http://dx.doi.org/10.1016/s1474-6670(17)51322-1.

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18

Foley, Michael W., Navin R. Ramharack, and Brian R. Copeland. "Comparison of PI Controller Tuning Methods." Industrial & Engineering Chemistry Research 44, no. 17 (2005): 6741–50. http://dx.doi.org/10.1021/ie040258o.

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19

Bouserhane, Ismail K., Abdeldjebar Hazzab, Abdelkrim Boucheta, Benyounes Mazari, and Rahli Mostefa. "Optimal Fuzzy Self-Tuning of PI Controller Using Genetic Algorithm for Induction Motor Speed Control." International Journal of Automation Technology 2, no. 2 (2008): 85–95. http://dx.doi.org/10.20965/ijat.2008.p0085.

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We present induction motor speed control using optimal PI controller fuzzy gain scheduling. To improve PI controller performance, we designed fuzzy PI controller gain tuning for indirect-field oriented IMspeed control using fuzzy rules on-line to adapt PI controller parameters based on error and its first time derivative. To overcome the major disadvantage of fuzzy logic control, i.e., the lack of design technique, we propose optimization of fuzzy logic tuning parameters using a genetic algorithm. Optimally designed fuzzy logic provides suitable PI controller gain to achieve the desired speed

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20

Poola, Rajesh, and Tsuyoshi Hanamoto. "Automated QFT-Based PI Tuning for Speed Control of SynRM Drive with Analytical Selection of QFT Control Specifications." Energies 15, no. 2 (2022): 642. http://dx.doi.org/10.3390/en15020642.

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The gains of PI controllers, used in the cascaded speed control of synchronous reluctance motors (SynRMs), are synthesized using quantitative feedback theory (QFT). A systematic design approach is employed to quantitatively determine the PI controller gains in terms of speed and current loops, using a mathematical model of the SynRM. Further, to make the QFT design a more transparent method, an analytical procedure using the frequency domain is attempted to design the QFT bounds as well as the initial search space of the optimization algorithm used in automatic loop shaping. The effectiveness

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21

Sule, Aliyu Hamza, Ahmad Safawi Mokhtar, Jasrul Jamani Bin Jamian, Attaullah Khidrani, and Raja Masood Larik. "Optimal tuning of proportional integral controller for fixed-speed wind turbine using grey wolf optimizer." International Journal of Electrical and Computer Engineering (IJECE) 10, no. 5 (2020): 5251. http://dx.doi.org/10.11591/ijece.v10i5.pp5251-5261.

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The need for tuning the PI controller is to improve its performance metrics such as rise time, settling time and overshoot. This paper proposed the Grey Wolf Optimizer (GWO) tuning method of a Proportional Integral (PI) controller for fixed speed Wind Turbine. The objective is to overcome the limitations in using the PSO and GA tuning methods for tuning the PI controller, such as quick convergence occurring too soon into a local optimum, and the controller step input response. The GWO, the Particle Swarm Optimization (PSO), and the Genetic Algorithm (GA) tuning methods were implemented in the

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22

Aliyu, Hamza Sule, Safawi Mokhtar Ahmad, Jamani Bin Jamian Jasrul, Khidrani Attaullah, and Masood Larik Raja. "Optimal tuning of proportional integral controller for fixed-speed wind turbine using grey wolf optimizer." International Journal of Electrical and Computer Engineering (IJECE) 10, no. 5 (2020): 5251–61. https://doi.org/10.11591/ijece.v10i5.pp5251-5261.

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The need for tuning the PI controller is to improve its performance metrics such as rise time, settling time and overshoot. This paper proposed the Grey Wolf Optimizer (GWO) tuning method of a Proportional Integral (PI) controller for fixed speed Wind Turbine. The objective is to overcome the limitations in using the Particle Swarm Optimization (PSO) and Genetic Algorithm (GA) tuning methods for tuning the PI controller, such as quick convergence occurring too soon into a local optimum, and overshoot of the controller step input response. The GWO, the PSO, and the GA tuning methods were implem

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23

Tanawat, Chalardsakul, Piliyasilpa Chotnarin, and Sukontanakarn Viroch. "TMS320F28379D microcontroller for speed control of permanent magnet direct current motor." IAES International Journal of Artificial Intelligence (IJ-AI) 13, no. 3 (2024): 2816–28. https://doi.org/10.11591/ijai.v13.i3.pp2816-2828.

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This paper aims to study the behavior of the proportional integral derivative (PID) and the fuzzy-based tuning PI-D controller for speed control of a permanent magnet direct current (PMDC) motor. The proposed method used a fuzzy-based tuning PI-D controller with a MATLAB/Simulink program to design and real-time implement a TMS320F28379D microcontroller for speed control of a PMDC motor. The performance of the study designed fuzzy-based tuning PI-D and PID controllers is compared and investigated. The fuzzy logic controller applies the controlling voltage based on motor speed errors. Finally, t

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24

Chalardsakul, Tanawat, Chotnarin Piliyasilpa, and Viroch Sukontanakarn. "TMS320F28379D microcontroller for speed control of permanent magnet direct current motor." IAES International Journal of Artificial Intelligence (IJ-AI) 13, no. 3 (2024): 2816. http://dx.doi.org/10.11591/ijai.v13.i3.pp2816-2828.

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<p><span>This paper aims to study the behavior of the <a name="_Hlk169245133"></a>proportional integral derivative (PID) and the fuzzy-based tuning PI-D controller for speed control of a permanent magnet direct current (PMDC) motor. The proposed method used a fuzzy-based tuning PI-D controller with a MATLAB/Simulink program to design and real-time implement a TMS320F28379D microcontroller for speed control of a PMDC motor. The performance of the study designed fuzzy-based tuning PI-D and PID controllers is compared and investigated. The fuzzy logic controller applies th

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25

Praptodiyono, Supriyanto, Hari Maghfiroh, and Chico Hermanu. "BLDC Motor Control Optimization Using Optimal Adaptive PI Algorithm." Jurnal Elektronika dan Telekomunikasi 20, no. 2 (2020): 47. http://dx.doi.org/10.14203/jet.v20.47-52.

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The main problem of using a Proportional Integral (PI) Controller in Brushless Direct Current (BLDC) motor speed control is tuning the PI’s parameter and its performance cannot adapt to the system behavior changes. Particle Swarm Optimization (PSO) has been chosen to optimize the tuning. Fuzzy Logic Controller (FLC) is used to online tuning PI’s parameters to adapt to system conditions. Optimal adaptive PI, which combines the PSO method and FLC method to tune PI, is proposed. It was successfully implemented in the simulation environment. The test was carried out in three conditions: step respo

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26

Setiawan, Bima Dwi Priya, Muhammad Nizar Habibi, Novie Ayub Windarko, and Sutedjo. "PENGATURAN KECEPATAN MOTOR INDUKSI DENGAN METODE Vf DAN KONTROL PI-FUZZY." Suara Teknik : Jurnal Ilmiah 11, no. 2 (2020): 1. http://dx.doi.org/10.29406/stek.v11i2.2047.

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Motor induksi memiliki konstruksi yang kokoh dan perewatan sederhana sehingga motor induksi menjadi kebutuhan utama di industri. Pengaturan kecepatan motor induksi merupakan salah satu kondisi operasi yang sering digunakan sehingga diperlukan kontrol umpan balik dengan tingkat error yang rendah.. Untuk memenuhi hal tersebut telah diterapkan seperti kontrol PI. Kontrol PI dapat mencakup berbagai kondisi operasi yang lebih luas dan mudah disesuaikan, namun kontrol ini masih memiliki kelemahan dalam proses tuning nilai parameter. Meskipun terdapat metode dalam proses tuning parameter PI, metode t

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27

Şenol, Bilal, and Uğur Demiroğlu. "Optimizing PI Controller for Stability and Overshoot in Step Response Using GA and PSO Techniques, A Comparative Study." International Scientific and Vocational Studies Journal 9, no. 1 (2025): 12–23. https://doi.org/10.47897/bilmes.1578027.

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In this paper, we present a comprehensive and in-depth investigation on the optimization of Proportional-Integral (PI) controller tuning for achieving stability and desired overshoot in the step response. The main objective of this study is to compare the effectiveness of Genetic Algorithm (GA) and Particle Swarm Optimization (PSO) techniques in finding the optimal parameters for the PI controller. The PI controller is a widely used control algorithm that plays a crucial role in many industrial processes. Its tuning greatly affects the system's performance, particularly in terms of stability a

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28

Pan, Feng Ping, Hong Kai Liao, Jia Luo, and Xi Zhang. "Optimal PI Controller Tuning Based on ITAE Criterion for Low-Order Systems with Large Time Delay." Applied Mechanics and Materials 411-414 (September 2013): 1716–19. http://dx.doi.org/10.4028/www.scientific.net/amm.411-414.1716.

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For low order process with large time delay, a kind of optimal PI controller tuning method is proposed based on generalized Hermite-Biehler theorem and Genetic Algorithm. Firstly, the stable region of PI controller is obtained by using the generalized Hermite-Biehler theorem. Then the optimum parameters are selected from this region based on ITAE criterion and genetic algorithm. A tuning formula is obtained by nonlinear fitting of optimization result, which has the capability to cover the variety of normalized time delays up to 100. Simulation of Monte-Carlo stochastic experiment indicates tha

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29

P, Aravind, and M.Girirajkumar S. "Performance Optimization of PI Controller in Non Linear Process using Genetic Algorithm." Performance Optimization of PI Controller in Non Linear Process using Genetic Algorithm 3, no. 5 (2013): 1968–72. https://doi.org/10.5281/zenodo.5517052.

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Recently, through the use of soft computing techniques fine tuning of PID controller parameters are carried out for non linear process. In this paper the Genetic Algorithm (GA) optimization technique, is successfully applied for tuning PI controller used in conical tank level process and hence to minimize the integral time absolute error (ITAE). A conical tank level process is represented as first order plus dead time transfer function. It is obtained by deriving mathematical differential equation and implemented in MATLAB. The main objective is to obtain a minimum rise time, minimum setting t

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30

Romasevych, Yuriy, Vyacheslav Loveykin, A. Liashko, and V. Makarets. "Development of PI-controller optimal tuning method." Avtomatizacìâ virobničih procesìv u mašinobuduvannì ta priladobuduvannì 53 (2019): 56–65. http://dx.doi.org/10.23939/istcipa2019.53.056.

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31

Däubler, L., C. Bessai, and O. Predelli. "Tuning Strategies for Online-Adaptive PI Controllers." Oil & Gas Science and Technology - Revue de l'IFP 62, no. 4 (2007): 493–500. http://dx.doi.org/10.2516/ogst:2007045.

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32

SATO, Kazuya, Jia-Chun FAN, and Toshihiro KOBAYASHI. "A PI Controller with Adaptive Parameter Tuning." Transactions of the Society of Instrument and Control Engineers 34, no. 11 (1998): 1632–38. http://dx.doi.org/10.9746/sicetr1965.34.1632.

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33

Airikka, Pasi, and Pentti Lautala. "Method for Tuning Setpoint Weighted PI Controllers." IFAC Proceedings Volumes 33, no. 13 (2000): 49–54. http://dx.doi.org/10.1016/s1474-6670(17)37164-1.

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34

Villagrán, Victor, and Daniel Sbarbaro. "Tuning Fuzzy PI Controllers by Iterative Learning." IFAC Proceedings Volumes 33, no. 4 (2000): 571–76. http://dx.doi.org/10.1016/s1474-6670(17)38304-0.

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35

Lo, W. L., A. B. Rad, and K. M. Tsang. "Auto-tuning of output predictive PI controller." ISA Transactions 38, no. 1 (1999): 25–36. http://dx.doi.org/10.1016/s0019-0578(98)00042-1.

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36

He, Jian-Bo, Qing-Guo Wang, and Tong-Heng Lee. "PI/PID controller tuning via LQR approach." Chemical Engineering Science 55, no. 13 (2000): 2429–39. http://dx.doi.org/10.1016/s0009-2509(99)00512-6.

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37

Ramkumar, K. B., and M. Chidambaram. "Fuzzy self-tuning PI controller for bioreactors." Bioprocess Engineering 12, no. 5 (1995): 263–67. http://dx.doi.org/10.1007/bf00369500.

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38

Jussila, T. T., J. T. Tanttu, M. V. Piirto, and H. N. Koivo. "An Extended Self-Tuning Multivariable PI Controller." IFAC Proceedings Volumes 23, no. 1 (1990): 289–94. http://dx.doi.org/10.1016/s1474-6670(17)52735-4.

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39

Clarke, D. W. "PI auto-tuning during a single transient." IEE Proceedings - Control Theory and Applications 153, no. 6 (2006): 671–83. http://dx.doi.org/10.1049/ip-cta:20050179.

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40

Pitchai, Ravindra. "ANALYSIS OF NON-INTERACTING LEVEL PROCESS USING VARIOUS PI CONTROL SETTINGS: A COMPARATIVE STUDY." American Journal of Interdisciplinary Innovations and Research 05, no. 05 (2023): 10–12. http://dx.doi.org/10.37547/tajiir/volume05issue05-03.

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This study presents a comparative analysis of different proportional-integral (PI) controller tuning methods for the control of a non-interacting liquid level process. Four different PI controller tuning methods, Ziegler-Nichols, Cohen-Coon, Tyreus-Luyben, and Internal Model Control, are evaluated based on their ability to track setpoint changes and reject disturbances. The simulation results show that all four tuning methods can provide satisfactory performance, but the Internal Model Control method outperforms the others in terms of all performance metrics evaluated. The Ziegler-Nichols meth

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41

Wang, Hsiu Ping, and Yu Feng Chang. "Weighted Tuning PI Controller for MRAS Sensorless Induction Motor Drive." Applied Mechanics and Materials 220-223 (November 2012): 1066–70. http://dx.doi.org/10.4028/www.scientific.net/amm.220-223.1066.

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The paper presents a weighted tuning PI controller for speed estimating of induction motor using model reference adaptive system (MRAS) approach in a direct torque control system. The performance of speed controller affects the performance of sensorless each other. The objective of presented weighted tuning PI controller is to improve the performance of induction motor drive and enhance the performance of speed estimation. The presented controller is based on Ziegler-Nichols (Z-N) tuning formula with weighted tuning. The method improves the problem of parameter choice, reduces the over shoot a

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Kong, Ling Lin, Duan Neng Li, and Ke Li. "The Effects of Tuning Controller Gains for Servo Feed Unit Positioning Accuracy." Advanced Materials Research 317-319 (August 2011): 1974–78. http://dx.doi.org/10.4028/www.scientific.net/amr.317-319.1974.

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Positioning accuracy of servo feed unit makes an important effect to processing precision in CNC machine tools. The effects in positioning accuracy mainly are the precisions of mechanism and servo system. And the actions of tuning controller gains should directly affect the servo system precision. This paper, a servo system model and its P/PI controller has been formulated and simulated, using an AC servo feed unit as a tested. Through tuning gains of P/PI controller, analyses and control the effects for the feed unit positioning accuracy. Ultimately, experiments have been carried out to test

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Padiachy, Vadan, and Utkal Mehta. "Novel Fractional-Order Proportional-Integral Controller for Hybrid Power System with Solar Grid and Reheated Thermal Generator." Solar 3, no. 2 (2023): 298–321. http://dx.doi.org/10.3390/solar3020018.

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This paper presents a new fractional-order proportional-integral, (PI)λ (FO[PI]) type structure to investigate the load frequency control (LFC) problem. In the literature, some controllers’ extensive tuning options may slow or complicate the optimization process. Due to the intricacy of the tuning, even if there are fewer tuning parameters, a robust structure can be obtained. The (PI)λ structure deviates from the standard FOPI, integer PID, or PI-PD controllers with the same or fewer tuning parameters. The efficacy of a tri-parametric fractional-order controller is examined on a two-area inter

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Karthika, R., and V. Suresh Kumar. "Artificial Bee Colony Algorithm-Based Shunt Active Filter for Sinusoidal Supply and Trapezoidal Supply." Journal of Circuits, Systems and Computers 27, no. 01 (2017): 1850016. http://dx.doi.org/10.1142/s0218126618500160.

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In this paper, a DC-link voltage tuning algorithm is introduced to control the shunt active filter (SAF) with sinusoidal and trapezoidal power supplies. The purpose of the proposed optimization algorithm is for tuning the PI controller and reducing the harmonics level. Artificial bee colony (ABC) algorithm is introduced for tuning the gain of the controller and the voltage variation of power converter by using PWM pulses. It regulates the DC-link voltage as per the signal harmonics and the active power loss of the system is reduced. Therefore, the accurate compensation current is injected by t

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45

Alyoussef, Fadi, Ibrahim Kaya, and Ahmad Akrad. "Robust PI-PD Controller Design: Industrial Simulation Case Studies and a Real-Time Application." Electronics 13, no. 17 (2024): 3362. http://dx.doi.org/10.3390/electronics13173362.

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PI-PD controllers have superior performance compared to traditional PID controllers, especially for controlling unstable and integrating industrial processes with time delays. However, computing the four tuning parameters of this type of controller is not an easy task. Recently, there has been significant interest in determining the tuning rules for PI-PD controllers that utilize the stability region. Currently, most tuning rules for the PI-PD controller are presented graphically, which can be time-consuming and act as a barrier to their industrial application. There is a lack of analytical tu

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46

Precup, Radu-Emil, Stefan Preitl, Claudia-Adina Bojan-Dragos, Elena-Lorena Hedrea, Raul-Cristian Roman, and Emil M. Petriu. "A LOW-COST APPROACH TO DATA-DRIVEN FUZZY CONTROL OF SERVO SYSTEMS." Facta Universitatis, Series: Mechanical Engineering 20, no. 1 (2022): 021. http://dx.doi.org/10.22190/fume220111005p.

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Servo systems become more and more important in control systems applications in various fields as both separate control systems and actuators. Ensuring very good control system performance using few information on the servo system model (viewed as a controlled process) is a challenging task. Starting with authors’ results on data-driven model-free control, fuzzy control and the indirect model-free tuning of fuzzy controllers, this paper suggests a low-cost approach to the data-driven fuzzy control of servo systems. The data-driven fuzzy control approach consists of six steps: (i) open-loop dat

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47

Boussaid, Brahim, Abdelkader Harrouz, Djamel Benmenine, Fadila Tahiri, and Fatiha Bekraoui. "Optimal PI Parameters Tuning for a DC-DC Boost Converter." Algerian Journal of Renewable Energy and Sustainable Development 3, no. 2 (2021): 223–29. http://dx.doi.org/10.46657/ajresd.2021.3.2.11.

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This paper designs a nonlinear PI-type controller for the robust control of a boost DC-DC converter using a particle swarm optimization (PSO) algorithm for increasing the output voltage of the photovoltaic (PV) system. In addition, the PI controller is used to tracking the maximum power from the PV panel, at different atmospheric condition. For tuning PI controllers is a tedious work and it is difficult to tune the PI gains optimally due to the nonlinearity. This paper presents an approach to use the particle swarm optimization algorithm to design the optimal PI controllers. The Simulations re

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Stopakevych, Andrii, Oleksii Stopakevych, Anatolii Tigarev, and Olena Vorobiova. "Automatic Re-tuning of Poor-Performing PI-based Control Systems." Problems of the Regional Energetics, no. 2(62) (April 2024): 164–79. http://dx.doi.org/10.52254/1857-0070.2024.2-62.14.

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The goal of the work is to create a new method for automatic re-tuning of PI controllers. It is achieved by solving the following problems. The first problem is to develop a method to identify a FOPDT estimation model by analyzing the dynamics of a control loop with a PI controller. The second problem is to demonstrate that the use of the estimated model allows to obtain better processes in control systems with PI controllers by comparing the resulting FOPDT model with a number of reduced models. The third problem is to develop a five-stage algorithm for automatic re-tuning of PI controllers.

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Romasevych, Yu, V. Loveikin, A. Liashko, and I. Bolbot. "Development of software for the pi-controllers optimal tining." Energy and automation, no. 4(56) (August 30, 2021): 99–112. http://dx.doi.org/10.31548/energiya2021.04.099.

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The use of software by automation engineers to adjust regulators is an important task. The aim of the study is to develop software for the tasks of optimal adjustment of PI controllers. In the article, based on the analysis of the functionality of modern software products for tuning automatic control systems, the main requirements for the development of software for tuning of automatic controllers have been stated. The interface of the developed PI-Tuner software product for determination of optimum values of coefficients of proportional and integral terms of the PI-controllers has been presen

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50

Bistak, Pavol, Igor Bélai, Igor Bélai, Damir Vrancic, and Mikulas Huba. "Application of a Fractional Order PI Controller for a Speed Servo Drive Control." Symmetry 16, no. 11 (2024): 1543. http://dx.doi.org/10.3390/sym16111543.

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This paper deals with the tuning of the parameters of a fractional-order PI controller for the speed control of an electric servo drive in which the torque is set by a torque generator. The controller parameters are tuned using the multiple dominant pole method (MDPM), while the fractional order integrator is approximated by the Oustaloup method. The input parameters required for tuning the controller using MDPM are calculated using the optimization algorithm presented in this paper. This algorithm selects the optimal parameters from a set of points in three-dimensional space, based on the sym

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