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Journal articles on the topic 'Controle PID fuzzy'

Author: Grafiati

Published: 4 June 2021

Last updated: 1 February 2022

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1

Mendonça, Márcio, Elton Carlos Correa, Ivan Rossato Chrun, and Orion Buss. "Sistema Supervisório e Controle Multivariável aplicando Controlador Fuzzy Ponderado-PID em um Processo de Fermentação Alcoólica." Semina: Ciências Exatas e Tecnológicas 36, no. 2 (2015): 95. http://dx.doi.org/10.5433/1679-0375.2015v36n2p95.

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<p align="left">Neste trabalho, é analisado, o controle de um sistema multivariável, um processo de fermentação alcoólica com fase não mínima. O controle é feito com controladores clássicos, Proporcional, Integrativo e Derivativo (PID) associado a um sistema supervisório baseado em Sistemas <em>Fuzzy</em>. O sistema <em>Fuzzy</em>, a priori, será um passador de <em>set-points </em>aos controladores PID, porém também agrega funções de proteção, como por exemplo, caso a biomassa esteja com valor zero ou muito próximo; O controlador <em>Fuzzy </em>altera a campanha para evitar ou amenizar o problema de paralisar a reação química na cuba. Duas arquiteturas de controle baseadas em sistemas de controle <em>Fuzzy </em>serão apresentadas, e comparadas em desempenho com controle clássico em campanhas diferentes. Um breve resumo sobre teoria <em>Fuzzy </em>e alguns trabalhos correlatos também serão apresentados. E, finalmente resultados de simulações, conclusões e futuros trabalhos encerram o artigo.</p>

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2

Suetake, Marcelo, Ivan Nunes da Silva, and Alessandro Goedtel. "Sistema fuzzy compacto embarcado em DSP e sua aplicação para controle V/f de motores de indução." Sba: Controle & Automação Sociedade Brasileira de Automatica 21, no. 3 (2010): 245–59. http://dx.doi.org/10.1590/s0103-17592010000300003.

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Este trabalho apresenta uma metodologia de implementação de algoritmos com estratégias fuzzy para sistemas embarcados em processadores digitais de sinais, cujo propósito de aplicação consiste no controle escalar de motores de indução trifásicos. A estratégia de controle adotada reside no ajuste da amplitude e freqüência (V/f) do sinal fundamental da tensão de alimentação do motor de indução, que foi modulado por largura de pulso aplicado a um inversor trifásico. Para tanto, o sistema de controle fuzzy foi integralmente embarcado em um processador digital de sinal empregando-se técnicas de simplificação que visam à redução dos requisitos de memória e custo computacional. O desempenho do controlador foi avaliado experimentalmente sob condições de variação de torque de carga aplicado ao eixo do motor de indução trifásico e referência de velocidade. Análises comparativas com as técnicas de controle PI e PID foram também realizadas com o propósito de validação da metodologia proposta.

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Leite, Manuela Souza, Ana Maria Frattini Fileti, and Flávio Vasconcelos da Silva. "Desenvolvimento e aplicação experimental de controladores fuzzy e convencional em um bioprocesso." Sba: Controle & Automação Sociedade Brasileira de Automatica 21, no. 2 (2010): 147–58. http://dx.doi.org/10.1590/s0103-17592010000200004.

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Este trabalho propõe a realização de um estudo comparativo do desempenho de controladores Fuzzy e convencional PID aplicados ao controle de temperatura de um processo de precipitação de bromelina do extrato aquoso de resíduos de abacaxi. Uma análise quantitativa da não-linearidade do processo foi realizada baseada na metodologia de curva de reação, aplicada em diferentes momentos da batelada, caracterizando o sistema por possuir diferente sensibilidade às ações de controle ao longo do tempo. O controlador convencional foi sintonizado a partir da aplicação das equações de Ziegler-Nichols aos parâmetros do processo obtidos nos instantes iniciais do experimento, seguido de sintonia fina por tentativa-e-erro. A sintonia do controlador Fuzzy consistiu na alteração do universo de discurso, na base de regras e na disposição das funções de pertinência, utilizando-se para isto o conhecimento obtido na análise das curvas de reação obtidas. Foi observado um melhor desempenho do controlador Fuzzy, apresentando menor valor da integral de erro absoluto multiplicado pelo tempo (ITAE), maior recuperação de atividade enzimática e menor consumo de energia elétrica para o resfriamento do sistema.

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Peng, Daogang, Hao Zhang, Hui Li, and Fei Xia. "Research of Networked Control System Based on Fuzzy Adaptive PID Controller." Journal of Advances in Computer Networks 2, no. 1 (2014): 44–47. http://dx.doi.org/10.7763/jacn.2014.v2.80.

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5

Zhang Andi, 张安迪, 张艳荣 Zhang Yanrong та 李涛 Li Tao. "论域可变的模糊PID控制在半导体激光器温度控制系统中的应用". Acta Optica Sinica 41, № 12 (2021): 1214003. http://dx.doi.org/10.3788/aos202141.1214003.

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6

Mizumoto, M. "Realization of PID controls by fuzzy control methods." Fuzzy Sets and Systems 70, no. 2-3 (1995): 171–82. http://dx.doi.org/10.1016/0165-0114(94)00215-s.

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7

Jong-Hwan Kim, Kwang-Choon Kim, and E. K. P. Chong. "Fuzzy precompensated PID controllers." IEEE Transactions on Control Systems Technology 2, no. 4 (1994): 406–11. http://dx.doi.org/10.1109/87.338660.

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8

Liu, Bin, Gang Yao, Xiao Bing Xiao, and Xu Sheng Yin. "The Research on Self-Adaptive Fuzzy PID Controller." Applied Mechanics and Materials 373-375 (August 2013): 1462–65. http://dx.doi.org/10.4028/www.scientific.net/amm.373-375.1462.

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The parameters of the traditional proportional-integral-derivative (PID) controller are hard to automatically adjust when the controlled object changes, which controls ineffective for time-varying, nonlinear system. Combine the fuzzy control and PID control, and use self-adaptive fuzzy control to achieve self-tuning PID parameters online. Using matlab simulation system, the results show that the self-adaptive fuzzy PID control effects have been improved than the conventional PID control.

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9

Sailan, Khaled, Klaus D. Kuhnert, and Hradik Karelia. "Modeling, Design and Implement of Steering Fuzzy PID Control System for DORIS Robot." International Journal of Computer and Communication Engineering 3, no. 1 (2014): 57–62. http://dx.doi.org/10.7763/ijcce.2014.v3.292.

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10

I Srinu, I. Srinu, and Dr M. Siva Kumar Dr. M Siva Kumar. "Design and Application of PID Controller using Fuzzy logic for better Disturbance Rejection." International Journal of Scientific Research 2, no. 11 (2012): 168–72. http://dx.doi.org/10.15373/22778179/nov2013/55.

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11

Irmawan, Erwhin, and Erwan Eko Prasetiyo. "Kendali Adaptif Neuro Fuzzy PID untuk Kestabilan Terbang Fixed Wing UAV (Adaptive Control of Neuro Fuzzy PID for Fixed Wing UAV Flight Stability)." Jurnal Nasional Teknik Elektro dan Teknologi Informasi 9, no. 1 (2020): 73–78. http://dx.doi.org/10.22146/jnteti.v9i1.142.

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Unmanned Aerial Vehicle (UAV), especially fixed wing, are widely used to carry out various missions, namely civil and military missions. To support the implementation of this mission, it is necessary to develop an intelligent automatic control system (autopilot). In this paper, an autopilot system with adaptive neuro fuzzy PID control is developed to control lateral (pitch) and longitudinal (roll) motion, by taking advantage of PID, fuzzy, and neural network control. Therefore, robust controls which can handle non-linear conditions can be formed. This paper aims to determine the performance of adaptive control of neuro fuzzy PID controllers for longitudinal and lateral motion on UAV. The result shows that adaptive control of neuro fuzzy PID are able to control the lateral and longitudinal motion of the aircraft and able to compensate for interferences from environmental disturbances in flying condition, such as changes in direction and wind speed that causes changes in aircraft attitude. The control characteristics of neuro fuzzy PID adaptive control in lateral and longitudinal motion are relatively similar. Adaptive control of neuro fuzzy PID has better performance than fuzzy PID control, i.e., faster settling time and lower percentage of maximum overshoot.

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12

Kazemian, H. B. "Developments of fuzzy PID controllers." Expert Systems 22, no. 5 (2005): 254–64. http://dx.doi.org/10.1111/j.1468-0394.2005.00316.x.

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13

Vassilyev, S. N., Yu I. Kudinov, F. F. Pashchenko, et al. "Intelligent Control Systems and Fuzzy Controllers. II. Trained Fuzzy Controllers, Fuzzy PID Controllers." Automation and Remote Control 81, no. 5 (2020): 922–34. http://dx.doi.org/10.1134/s0005117920050112.

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14

Yang, Yang, Yan Chen, Hao Sun, and Jin Huang. "The Rotation Speed Control of DC Servo Motor Based on Fuzzy-PI Dual Mode." Applied Mechanics and Materials 454 (October 2013): 34–38. http://dx.doi.org/10.4028/www.scientific.net/amm.454.34.

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The PID control algorithm is simple. But when it controls DC servo motor of nonlinearity, delay and strongly couple, its dynamic control performance is poor. Fuzzy control which is essentially equivalent to a nonlinear PD control has poor steady state performance. Fuzzy-PI dual-mode control simultaneously has PID control's steady-state performance and fuzzy control's dynamic performance. The step response simulation results shows that Fuzzy-PI controls rise time is 8sec and that the overshoot and steady-state error are close to 0. It has stronger anti-interference ability, higher dynamic performance and higher steady state performance.

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Cao, Kairui, Hak-Keung Lam, Xiaozhi Gao, and Athanasios Vasilakos. "H ∞ fuzzy PID control synthesis for Takagi–Sugeno fuzzy systems." IET Control Theory & Applications 10, no. 6 (2016): 607–16. http://dx.doi.org/10.1049/iet-cta.2015.0513.

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16

Mohan, B. M., and Arpita Sinha. "Analytical Structures for Fuzzy PID Controllers?" IEEE Transactions on Fuzzy Systems 16, no. 1 (2008): 52–60. http://dx.doi.org/10.1109/tfuzz.2007.894974.

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17

Alkargole, Hazim M., Abbas S. Hassan, and Raoof T. Hussein. "Analyze and Evaluate the Performance Velocity Control in DC Motor." Radioelectronics. Nanosystems. Information Technologies 12, no. 4 (2020): 507–16. http://dx.doi.org/10.17725/rensit.2020.12.507.

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A mathematical model of controlling the DC motor has been applied in this paper. There are many and different types of controllers have been used with purpose of analyzing and evaluating the performance of the of DC motor which are, Fuzzy Logic Controller (FLC), Linear Quadratic Regulator (LQR), Fuzzy Proportional Derivative (FPD) ,Proportional Integral Derivative (PID), Fuzzy Proportional Derivative with integral (FPD plus I) , and Fuzzy Proportional Integral (FPI) with membership functions of 3*3, 5*5, and 7*7 rule bases. The results show that the (FLC) controller with 5*5 rule base provides the best results among all the other controllers to design the DC motor controller.

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18

Zhou, Xiao Bin, Li Zeng, Zheng Jie Xu, Dao Tian Hu, and Yan Neng Yang. "Fuzzy Control Strategy of the Mixed Magnetic Levitation Bearing Rotor System." Applied Mechanics and Materials 184-185 (June 2012): 1566–69. http://dx.doi.org/10.4028/www.scientific.net/amm.184-185.1566.

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This paper proposes a Fuzzy Control strategy of magnetic levitation bearing rotor according to the mixed magnetic levitation bearing. Combined with electromagnetic winding working principle, this paper uses the self-sensing displacement self-diagnosing system instead of the special displacement sensor, makes a no sensor self-diagnosing magnetic suspension control system, controls the system by the fuzzy PID (proportional, integral and differential) controller, and builds a mathematical model of the control system. Analyzing the fuzzy rules and fuzzy control principle of the fuzzy PID controller setting parameters, this paper has a fuzzy simulation for the system.

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19

Kusuma, Panca Agung, and Andi Dharmawan. "Pengendalian Kestabilan Ketinggian pada Penerbangan Quadrotor dengan Metode PID Fuzzy." IJEIS (Indonesian Journal of Electronics and Instrumentation Systems) 7, no. 1 (2017): 61. http://dx.doi.org/10.22146/ijeis.15456.

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Quadrotor is a kind of unmanned aerial vehicle that have the ability to take of vertically and maintaining its position while flying mid-air. Flying a quadrotor sometimes needs a stable altitude to perform a specific mission. A stable altitude will make easier for pilot to control the movement of the quadrotor to certain direction.This study designed and implemented a system that can stabilises the altitude of a quadrotor by using Fuzzy-PID method. Altitude control system needed to help pilot controls the altitude stability without adjusting the throttle. Control with PID method is a common control system to be implemented on a quadrotor. This control system has a constant that can be tuned with fuzzy logic with linguistic approach to improve the response time when compensating an error. The result of this study shows that Fuzzy PID control method generate a better response time compared with the PID-only method. The implementation of PID control generate an altitude stabilisation with a mean value steady state error of ±1,86 cm, whereas the PID Fuzzy generate a mean value of steady state error of ±1,22 cm.

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20

Zhang, Yi Hui, and Le Peng Song. "Brushless DC Motor Speed Control System Based on Fuzzy PID Control." Advanced Materials Research 989-994 (July 2014): 3172–76. http://dx.doi.org/10.4028/www.scientific.net/amr.989-994.3172.

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Brushless DC mo tor speed control system is a multivariate, strong coupling, non linear, time-varying complex system, but adopting traditional PID control method to carry ou t control is difficult to achieve good control effect A kind of PID controller with fuzzy algorithm setting on-line PID parameters automatically was designed and applied in brush less DC motor speed control system, using the voltage , speed and torque equation of brush less DC motor, according to the parameters of the mo tor, the controler adopts fuzzy theory to adjust the PID parameters, in order to obtain high-precision speed control Results of simulation experiment show that the fuzzy PID control method compared with normal PID control is with better control performance, non overshoot quick velocity response, higher control precision and good rubustness, which is insensitive to the parameter chattering and many disturbances.

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21

Visioli, A. "Tuning of PID controllers with fuzzy logic." IEE Proceedings - Control Theory and Applications 148, no. 1 (2001): 1–8. http://dx.doi.org/10.1049/ip-cta:20010232.

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22

Irianto, Irianto, Farid Dwi Murdianto, Epyk Sunarno, and Dewinta Dwi Proboningtyas. "Comparison Method of PI, PID and Fuzzy Logic Controller to Maintain Speed Stability in Single Phase Induction Motors." INTEK: Jurnal Penelitian 8, no. 1 (2021): 7. http://dx.doi.org/10.31963/intek.v8i1.2687.

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Induction motor speed control is one of the operating conditions that is often used so that feedback with a low error rate is required. To fulfill this, PI and PID controls have been implemented for single phase induction motors. This control has parameters, namely Kp, Ki and Kd. PI and PID controls can cover a variety of desired response conditions, but these controls still have weaknesses in the tuning process. The tuning process used still has a fairly large error value. So in this case we need an intelligent control to meet the desired motor speed response specifications. The performance of motor speed regulation was evaluated using a comparison between PI and PID control with Fuzzy in a closed loop. With a setting point of 1500 rpm, for PI control, with Kp = 7.32 and Ki = 0.005 can produce motor speeds up to 1499. While PID control with Kp = 0.95, Ki = 0.005 and Kd = 0.04 can produce similar speeds. 1492. Fuzzy control can produce an output of 1490 rpm. Fuzzy control is able to produce a settling time of 0.25 seconds and a steady error of 0.67%.

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23

Hayashi, Kenichiro, Akifumi Otsubo, Shuta Murakami, and Mikio Maeda. "Realization of nonlinear and linear PID controls using simplified indirect fuzzy inference method." Fuzzy Sets and Systems 105, no. 3 (1999): 409–14. http://dx.doi.org/10.1016/s0165-0114(97)00248-0.

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Hansson, A., P. Gruber, and J. Tödtli. "Fuzzy anti-reset windup for PID controllers." Control Engineering Practice 2, no. 3 (1994): 389–96. http://dx.doi.org/10.1016/0967-0661(94)90775-7.

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25

Wong, Pak Kin, Shao Jia Huang, Tao Xu, Hang Cheong Wong, and Zheng Chao Xie. "Design of a New Suspension System Controlled by Fuzzy-PID with Wheelbase Preview." Applied Mechanics and Materials 192 (July 2012): 106–10. http://dx.doi.org/10.4028/www.scientific.net/amm.192.106.

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This paper studies a new active vehicle suspension controlled by Fuzzy-PID controller with wheel base preview. By this new algorithm, the fuzzy controller controls the parameters of the PID in time .Then the wheelbase preview is integrated to ensure the future road information is combined with the current state of the vehicle effectively. A sensor is placed on the front suspension collects and feeds forward the preview information as an input to the rear suspension system . MATLAB simulations show that using such control strategy can obtain a low noise and better robustness performance than the traditional PID control algorithm.

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26

Yin, Junnan, Dequan Zhu, Juan Liao, Guangyue Zhu, Yao Wang, and Shun Zhang. "Automatic Steering Control Algorithm Based on Compound Fuzzy PID for Rice Transplanter." Applied Sciences 9, no. 13 (2019): 2666. http://dx.doi.org/10.3390/app9132666.

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In order to realize automatic steering controls of rice transplanters in paddy fields, an automatic steering control algorithm is essential. In this study, combining the fuzzy control with the proportional-integral-derivative (PID) control and the kinematics model, a compound fuzzy PID controller was proposed to adjust the real time data of the PID parameters for the automatic steering control. The Kubota SPU-68C rice transplanter was then modified with the new controller. Next, an automatic steering control experimental with the modified transplanter was carried out under two conditions of linear tracking and headland turning in verifying the automatic steering effect of the transplanter in different steering angle situations. The results showed that the deviation with the new controller and the modified transplanter was acceptable, with maximum deviation in linear tracking of 7.5 cm, the maximum headland turning a deviation of 11.5 cm, and the average a deviation of less than 5 cm. In conclusion, within the allowable deviation range of the field operation of the rice transplanter, the proposed algorithm successfully realized automatic steering controls of the transplanter under different steering angles.

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Li, H. X., L. Zhang, K. Y. Cai, and G. Chen. "An Improved Robust Fuzzy-PID Controller With Optimal Fuzzy Reasoning." IEEE Transactions on Systems, Man and Cybernetics, Part B (Cybernetics) 35, no. 6 (2005): 1283–94. http://dx.doi.org/10.1109/tsmcb.2005.851538.

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28

Sakaguchi, Akihiro. "Fuzzy Control System and Its Application." Journal of Robotics and Mechatronics 1, no. 3 (1989): 227–32. http://dx.doi.org/10.20965/jrm.1989.p0227.

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There are many processes which have been automatically controlled by PID control or by the present control theory, but there are still many which are dependent on an operator's manipulations for control. Skilled operators, by their perceptions or experiences, can exercise controls even of such processes which have peculiar characteristics and for which it is difficult to provide automatic controls. The fuzzy control system was created to allow computers to apply the same controls as skilled operators do, by taking the operator's techniques into the computer. We shall describe below an outline of fuzzy control and the fuzzy control system we have developed.

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Bhateshvar, Y. K., and H. D. Mathur. "Power Frequency Oscillation Suppression Using Two-Stage Optimized Fuzzy Logic Controller for Multigeneration System." Advances in Fuzzy Systems 2016 (2016): 1–13. http://dx.doi.org/10.1155/2016/8308109.

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This paper attempts to develop a linearized model of automatic generation control (AGC) for an interconnected two-area reheat type thermal power system in deregulated environment. A comparison between genetic algorithm optimized PID controller (GA-PID), particle swarm optimized PID controller (PSO-PID), and proposed two-stage based PSO optimized fuzzy logic controller (TSO-FLC) is presented. The proposed fuzzy based controller is optimized at two stages: one is rule base optimization and other is scaling factor and gain factor optimization. This shows the best dynamic response following a step load change with different cases of bilateral contracts in deregulated environment. In addition, performance of proposed TSO-FLC is also examined for±30%changes in system parameters with different type of contractual demands between control areas and compared with GA-PID and PSO-PID. MATLAB/Simulink® is used for all simulations.

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30

Mu, Honghui. "Application of adaptived Fuzzy PID control in new heating system." International Journal of Control and Automation 9, no. 12 (2016): 417–26. http://dx.doi.org/10.14257/ijca.2016.9.12.34.

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31

Lee, Ching-Hung, Yi-Hsiung Lee, and Ching-Cheng Teng. "A Novel Robust Pid Controller Design By Fuzzy Neural Network." Asian Journal of Control 4, no. 4 (2008): 433–38. http://dx.doi.org/10.1111/j.1934-6093.2002.tb00084.x.

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32

Paul, Satyam, Ajay Arunachalam, Davood Khodadad, Henrik Andreasson, and Olena Rubanenko. "Fuzzy Tuned PID Controller for Envisioned Agricultural Manipulator." International Journal of Automation and Computing 18, no. 4 (2021): 568–80. http://dx.doi.org/10.1007/s11633-021-1280-5.

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AbstractThe implementation of image-based phenotyping systems has become an important aspect of crop and plant science research which has shown tremendous growth over the years. Accurate determination of features using images requires stable imaging and very precise processing. By installing a camera on a mechanical arm driven by motor, the maintenance of accuracy and stability becomes non-trivial. As per the state-of-the-art, the issue of external camera shake incurred due to vibration is a great concern in capturing accurate images, which may be induced by the driving motor of the manipulator. So, there is a requirement for a stable active controller for sufficient vibration attenuation of the manipulator. However, there are very few reports in agricultural practices which use control algorithms. Although, many control strategies have been utilized to control the vibration in manipulators associated to various applications, no control strategy with validated stability has been provided to control the vibration in such envisioned agricultural manipulator with simple low-cost hardware devices with the compensation of non-linearities. So, in this work, the combination of proportional-integral-differential (PID) control with type-2 fuzzy logic (T2-F-PID) is implemented for vibration control. The validation of the controller stability using Lyapunov analysis is established. A torsional actuator (TA) is applied for mitigating torsional vibration, which is a new contribution in the area of agricultural manipulators. Also, to prove the effectiveness of the controller, the vibration attenuation results with T2-F-PID is compared with conventional PD/PID controllers, and a type-1 fuzzy PID (T1-F-PID) controller.

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Yin, Su Min, Rui Qiang Gao, Qing Zhou, and Ping Zhang. "Design of Battery Online Weighing System Based on PID-Fuzzy Control." Applied Mechanics and Materials 43 (December 2010): 44–48. http://dx.doi.org/10.4028/www.scientific.net/amm.43.44.

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According to the principle of manganese powder forming, a battery online weighing system with PLC-based PID-Fuzzy control algorithm was designed. In this paper, we focus on the need for a detailed description of hardware and software and matlab simulation of the system. The system uses touch screen for realization of human-machine interaction with high stability, and double closed-loop controls overcome the disadvantages of the traditional offline open-loop control, those greatly improve the efficiency and accuracy of manganese ring weighing.

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Chu, Cheng-Wei, Zhi-Chao Zhu, Hai-Tao Bian, and Jun-Cheng Jiang. "Design of self-heating test platform for sulfide corrosion and oxidation based on Fuzzy PID temperature control system." Measurement and Control 54, no. 5-6 (2021): 1082–96. http://dx.doi.org/10.1177/00202940211020338.

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In this paper, the changes of oxidation temperature of sulfide corrosion and the deficiency of distributed optical fiber application were analyzed. The test platform of oxidation temperature of sulfide corrosion was established, and the performance test of optical fiber and the simulation of oxidation temperature of sulfide corrosion were realized. The hardware part of the control system used STM32 as the controller, the software part was based on the process characteristics of the controlled object, using MATLAB to carry out the simulation of PID, fuzzy, fuzzy PD plus I, fuzzy PID algorithms, and their performance are evaluated using both single indexes and comprehensive indexes. The experimental results also showed that the proposed fuzzy PID can achieve better control performance with less overshoot and shorter setting time. Therefore, the fuzzy PID was chosen as the temperature control algorithm to build the optical fiber sensor test platform, and an alarm method for testing the oxidation temperature of large area sulfide corrosion based on the optical fiber performance was obtained. Then, considering the influence of spatial resolution on optical fiber sensor, this paper used piecewise PID to simulate the temperature rise process of three stages of sulfurization corrosion and oxidation. The results showed that the alarm method of oxidation temperature of sulfurization corrosion has limitations for small-scale oxidation of sulfurization corrosion, and it needed to be combined with machine learning to identify temperature anomaly.

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35

Sk, Roswaldi, and Julsam Julsam. "Rancang Bangun Tacho Generator Sebagai Sensor Kecepatan Pada Praktikum Kendali Motor-Motor Listrik." Elektron : Jurnal Ilmiah 1, no. 1 (2009): 1–10. http://dx.doi.org/10.30630/eji.1.1.4.

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The research implementing “Tachogenerator as the sensor for controller of Electric Motor Speed rotation at electric laboratories” Plant (Object controlled) is controlled by the electric motors speed rotation in AC or DC, by setting armature voltage of motor using digital PID controller and fuzzy logic from Personal Computer (PC). Although the load disturbances are changing significantly, but the motor speed rotation can be stabilized by feedback input from tachogenerator which are the focus in this research. PID controller dan fuzzy Logic controller applications are used to find the advantages the disadvatages or controll methode of motor reliability in dinamic controll (speed rotation) which cause the system more stabil. The result can be seen in maintained system responses and the system reliabilty against disturbances.

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36

You, Bo, Ming Qiao, Jiazhong Xu, and Dan Liu. "Low-Speed Control of Heavy-Load Transfer Robot with Long Telescopic Boom Based on Stribeck Friction Model." Mathematical Problems in Engineering 2012 (2012): 1–12. http://dx.doi.org/10.1155/2012/432129.

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The severe low-speed creep phenomenon occurs in the telescopic boom system of a heavy-load transfer robot with a long telescopic boom as a result of nonlinear friction. In order to improve control precision and operation performance at low speeds, we built a three-loop control nonlinear model of an AC servo motor with Stribeck friction disturbance. Traditional proportional-integral-derivative controller (PID) and fuzzy PID controls were, respectively, adopted in the position loop, and the control performance was simulated. The results showed that a system with fuzzy PID control eliminates “flat top” position tracking and “dead zone” speed tracking, which are generated by traditional PID, and thereby decreases the effect of friction on the performance of the servo system. This elimination also improved the tracking accuracy and robustness of the system.

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Lu, Jialiang, Guanrong Chen, and Hao Ying. "Predictive fuzzy PID control: theory, design and simulation." Information Sciences 137, no. 1-4 (2001): 157–87. http://dx.doi.org/10.1016/s0020-0255(01)00119-0.

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Ghavidel, Hesam Fallah, and Ali Akbarzadeh Kalat. "Synchronization Adaptive Fuzzy Gain Scheduling PID Controller for a Class of MIMO Nonlinear Systems." International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems 27, no. 04 (2019): 515–35. http://dx.doi.org/10.1142/s0218488519500235.

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This paper presents a new Synchronization Adaptive Fuzzy Gain Scheduling PID controller (SAFGS-PIDc) for a class of Multiple-input multiple-output (MIMO) nonlinear systems with uncertainties. To achieve better adaptation properties, weighting factor is adapted to sum together the adaptive fuzzy control scheme and Fuzzy Gain Scheduler PID control (FGS-PIDc) method, such that both controllers can be incorporated at the same time. The FGS adjusts online the parameters of the conventional PID controller, and an Indirect Adaptive Fuzzy control (IAFc) scheme that uses feedback error function as inputs is constructed. In addition, each subsystem of MIMO system is able to adaptively compensate for uncertainties and external disturbance. Also, a robust control term is designed that aims to provide added robustness in the presence of uncertainties. The proposed scheme can overcome the controller singularity problem. While the proposed controller scheme requires the uncertainties to be bounded, it does not require this bound to be known. Thus, this control law can be used for the systems that the system’s models are quite unknown. The proposed method guarantees the stability of the closed-loop system based on Lyapunov theory. Finally, simulation studies demonstrate the usefulness and effectiveness of the proposed technique for controlling nonlinear.

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Al Gizi, Abdullah J. H. "PLC Fuzzy PID Controller of MPPT of Solar Energy Converter." WSEAS TRANSACTIONS ON SYSTEMS AND CONTROL 16 (January 7, 2021): 1–20. http://dx.doi.org/10.37394/23203.2021.16.1.

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Development of the Maximum-Power-Point-Tracking (MPPT) scheme for solar mounts and rectifiers leftovers interesting. We design of high-sensitive fuzzy (HSF) Proportional-Integral-Derivative (PIDC) controller by means of Matlab and Programmable-Logic-Controllers (PLCs) for an adjusted of the MPPT scheme. This proposal is founded on a synergistic mixture of the Radial-Basis-Function-Neural Network (RBF-NN), Genetic Algorithm (GA), and Sugeno-Fuzzy-Logic (SFL) systems. The finest limits of PIDC and MPPT are strong-minded through optimization, wherever RBF-NN is adjusted by means of GA to reach the best key. Also, RBF-NN is rummage-sale to improve the PID limits (got from GA) for scheming HSFL-PIDC of the MPPT scheme. The HSFL-PIDC controller is extra planned to transfer in PLCs (STEP 75.5) for implementing the photovoltaic (PV) scheme. The all-inclusive scheme is extra tuned by solar limits under numerous operational circumstances to advance the solar recital in terms of accusing and correcting. The recital of the planned analog employed MPPT controller is assessed by interfacing it with a hardware prototype of dual photovoltaic (PV) scheme. The mixture controller configured the regulator signal founded on the interaction and in that way reduces the voltage mistake and the oscillation in the voltage regulator process. The achieved scheme is established to be well-organized and robust in refining solar charging and rectifying capability .

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Elmenfy, T. Hussein. "Design of Velocity PID-Fuzzy Power System Stabilizer Using Particle Swarm Optimization." WSEAS TRANSACTIONS ON SYSTEMS 20 (February 23, 2021): 9–14. http://dx.doi.org/10.37394/23202.2021.20.2.

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Power system stabilizer (PSS) is used to damping power system local and interarea modes of oscillation. It will act as additional signal through the generator excitation system which produces a component of electrical torque in phase with speed variation. Fuzzy controller is nonlinear. It is more difficult to set the fuzzy controller gains compared to conventional PID controller. This research proposed velocity (PID-FPSS) its gains are tuned off-line by particle swarm optimization techniques (PSO). The objectives are to damp local-area mode of oscillations that occur following power system disturbances. The effectiveness of the proposed technique is illustrated by applying the velocity PID-FPSS to a single-machine infinite bus power system that is typically used in the literature to test the performance of power system stabilizers. The simulation studies has been tested in MATLAB SIMULINK package . A comparison between the proposed PID-FPSS and a well-tuned and designed robust power system stabilizer (RPSS) confirms the superiority of the PID-FPSS.

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Xiaocheng, Ge, Xu Zhongming, Li Jingbo, and Zou Bowen. "Fuzzy PID Model -Based Study on Idle Control of Gas Engine." Open Cybernetics and Systemics Journal 8, no. 1 (2014): 660–66. http://dx.doi.org/10.2174/1874110x01408010660.

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Zhou, Nina, and Li Wang. "Application of Fuzzy Adaptive Tuning PID Control in the Boiler Deaerator System." International Journal of Control and Automation 9, no. 3 (2016): 309–18. http://dx.doi.org/10.14257/ijca.2016.9.3.28.

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Nam, Sae Kyu, and Wan Suk Yoo. "Fuzzy PID control with accelerated reasoning for d.c. servo motors." Engineering Applications of Artificial Intelligence 7, no. 5 (1994): 559–69. http://dx.doi.org/10.1016/0952-1976(94)90034-5.

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Khoury, G. M., M. Saad, H. Y. Kanaan, and C. Asmar. "Fuzzy PID Control of a Five DOF Robot Arm." Journal of Intelligent and Robotic Systems 40, no. 3 (2004): 299–320. http://dx.doi.org/10.1023/b:jint.0000038947.97195.22.

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Babes, Badreddine, Amar Boutaghane, Noureddine Hamouda, et al. "New Optimal Control of Permanent Magnet DC Motor for Photovoltaic Wire Feeder Systems." Journal Européen des Systèmes Automatisés 53, no. 6 (2020): 811–23. http://dx.doi.org/10.18280/jesa.530607.

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This article aims to improve the permanent magnet DC (PMDC) motor performance for photovoltaic (PV) wire-feeder systems (PVWFSs) of arc welding machines. The considered technique is designed by direct speed control based on optimal Fractional-order Fuzzy PID FO-Fuzzy-PID controller. The purpose is to ensure optimal control of wire feed speed reference to reduce torque ripples and hence, the performance of the WFS is improved. The dynamic reaction of the proposed solar PVWFS relies upon the scaling factors of FO-Fuzzy-PID controller, which are optimized by using teaching-learning algorithm based on Particle Swarm Optimization (PSO) method. The maximum power point tracking (MPPT) is achieved using an intelligent FO-Fuzzy-PID current controller based Perturb and Observe (P&O) MPPT algorithm. The PVWFS system incorporating the proposed method is tested and compared with the conventional PID control scheme under different weather conditions. The simulation of the proposed system by MATLAB\SIMULINK is carried out. The simulation results indicate the effectiveness of the considered control strategy in terms of the reduction in torque oscillations, optimizing electrical power and wire feed speed.

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Zhou, Xiangyang, Hao Gao, Yuan Jia, Lingling Li, Libo Zhao, and Ruifang Yu. "Parameter Optimization on FNN/PID Compound Controller for a Three-Axis Inertially Stabilized Platform for Aerial Remote Sensing Applications." Journal of Sensors 2019 (March 26, 2019): 1–15. http://dx.doi.org/10.1155/2019/5067081.

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This paper presents a composite parameter optimization method based on the chaos particle swarm optimization and the back propagation algorithms for a fuzzy neural network/proportion integration differentiation compound controller, which is applied for an aerial inertially stabilized platform for aerial remote sensing applications. Firstly, a compound controller combining both the adaptive fuzzy neural network and traditional PID control methods is developed to deal with the contradiction between the control precision and robustness due to disturbances. Then, on the basis of both the chaos particle swarm optimization and the back propagation compound algorithms, the parameters of the fuzzy neural network/PID compound controller are optimized offline and fine-tuned online, respectively. In this way, the compound controller can achieve good adaptive convergence so as to get high stabilization precision under the multisource dynamic disturbance environment. To verify the method, the simulations are carried out. The results show that the composite parameter optimization method can effectively enhance the convergence of the controller, by which the stabilization precision and disturbance rejection capability of the proposed fuzzy neural network/PID compound controller are improved obviously.

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Eltag, Khaled, Muhammad Shamrooz Aslamx, and Rizwan Ullah. "Dynamic Stability Enhancement Using Fuzzy PID Control Technology for Power System." International Journal of Control, Automation and Systems 17, no. 1 (2019): 234–42. http://dx.doi.org/10.1007/s12555-018-0109-7.

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Tang, Xiao, Chunsheng Wang, Yukun Hu, Zijian Liu, and Feiliang Li. "Adaptive Fuzzy PID Based on Granular Function for Proton Exchange Membrane Fuel Cell Oxygen Excess Ratio Control." Energies 14, no. 4 (2021): 1140. http://dx.doi.org/10.3390/en14041140.

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An effective oxygen excess ratio control strategy for a proton exchange membrane fuel cell (PEMFC) can avoid oxygen starvation and optimize system performance. In this paper, a fuzzy PID control strategy based on granular function (GFPID) was proposed. Meanwhile, a proton exchange membrane fuel cell dynamic model was established on the MATLAB/Simulink platform, including the stack model system and the auxiliary system. In order to avoid oxygen starvation due to the transient variation of load current and optimize the parasitic power of the auxiliary system and the stack voltage, the purpose of optimizing the overall operating condition of the system was finally achieved. Adaptive fuzzy PID (AFPID) control has the technical bottleneck limitation of fuzzy rules explosion. GFPID eliminates fuzzification and defuzzification to solve this phenomenon. The number of fuzzy rules does not affect the precision of GFPID control, which is only related to the fuzzy granular points in the fitted granular response function. The granular function replaces the conventional fuzzy controller to realize the online adjustment of PID parameters. Compared with the conventional PID and AFPID control, the feasibility and superiority of the algorithm based on particle function are verified.

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Li, C. Y., and W. X. Jing. "Fuzzy PID controller for 2D differential geometric guidance and control problem." IET Control Theory & Applications 1, no. 3 (2007): 564–71. http://dx.doi.org/10.1049/iet-cta:20060370.

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Zhang, Dong, and Di Jian Xu. "Fuzzy Controller for Synchronous Motor." Applied Mechanics and Materials 310 (February 2013): 536–39. http://dx.doi.org/10.4028/www.scientific.net/amm.310.536.

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The parameters of PID control couldn’t be adjusted on-line, so it also led to not good precision of velocity. This paper presents the implementation of adaptive fuzzy controller for accurate speed control of a permanent magnet synchronous motor (PMSM). The adaptive fuzzy controls were designed to solve above problems. According to the settling fuzzy rules, the voltage vectors are selected to control the inverter. The zero voltage space vector is used to keep the steady state and reduce frequency of inverter. The simulation results show that the proposed control scheme has stronger robustness and better performance than the conventional control scheme.

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