Relevant bibliographies by topics / PID; Control Systems; Tuning / Journal articles
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Journal articles on the topic 'PID; Control Systems; Tuning'

Author: Grafiati
Published: 4 June 2021
Last updated: 15 February 2022

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1

Liu, G. P., and S. Daley. "Optimal-Tuning PID Control for Industrial Systems." IFAC Proceedings Volumes 33, no. 4 (April 2000): 589–94. http://dx.doi.org/10.1016/s1474-6670(17)38307-6.

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2

Liu, G. P., and S. Daley. "Optimal-tuning PID control for industrial systems." Control Engineering Practice 9, no. 11 (November 2001): 1185–94. http://dx.doi.org/10.1016/s0967-0661(01)00064-8.

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3

Qian, Zheng Zai, Gong Cai Xin, and Jin Niu Tao. "Predictive Control Based on Fuzzy Expert PID Tuning Control." Advanced Materials Research 466-467 (February 2012): 1207–11. http://dx.doi.org/10.4028/www.scientific.net/amr.466-467.1207.

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In decade years, several simple methods for the automatic tuning of PID controllers have been proposed. There have been different approaches to the problem of deriving a PID-like adaptive controller. All of these can be classified into two broad categories: model-based; or expert systems. In this paper a new expert adaptive controller is proposed in which the underlying control law is a PID structure. The design is based on the fuzzy logic and the generalized predictive control theory. The proposed controller can be applied to a large class of systems which is model uncertainty or strong non-linearity. Simulation results have also been illustrated. It shows that the proposed expert PID-like controller performed well than generally used PID.
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4

Liu, G. P., and S. Daley. "Optimal-tuning nonlinear PID control of hydraulic systems." Control Engineering Practice 8, no. 9 (September 2000): 1045–53. http://dx.doi.org/10.1016/s0967-0661(00)00042-3.

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5

Aleksandrov, A. G., and M. V. Palenov. "Self-tuning PID/I controller." Automation and Remote Control 72, no. 10 (October 2011): 2010–22. http://dx.doi.org/10.1134/s000511791110002x.

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6

Atherton, D. P. "PID controller tuning." Computing & Control Engineering Journal 10, no. 2 (April 1, 1999): 44–50. http://dx.doi.org/10.1049/cce:19990202.

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7

Chen, Wen Wen, You Kuan Liu, Xiang Yu Tan, Jian Ping Sun, Shao Quan Zhang, Jiao Du, and Shuo Wang. "PID Parameter Optimization Based on Fuzzy Control." Advanced Materials Research 960-961 (June 2014): 1156–61. http://dx.doi.org/10.4028/www.scientific.net/amr.960-961.1156.

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PID controllers are widely used in industrial process control. The determination of the conventional PID controller parameter tuning is based on obtaining the mathematical model of controlled objects and according to certain rules, which is difficult to adapt to complex control systems. In this paper ,against its adverse effect parameter tuning, long time debugging, defects and poor adaptability of the controlled object,the fuzzy control and PID control are combined and the fuzzy PID controller is proposed. Then, I combine the examples of thermal power units using MATLAB to simulate. The simulation results show that the fuzzy self-tuning PID controller not only has the advantages of fuzzy control such as fast, adaptability, etc, but also the characteristics of high accuracy PID control, which make the system has a good control effect.
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Vrančić, Damir, Mikulaš Huba, and Paulo Moura Oliveira. "PID controller tuning for integrating processes." IFAC-PapersOnLine 51, no. 4 (2018): 586–91. http://dx.doi.org/10.1016/j.ifacol.2018.06.159.

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9

YAMAMOTO, Toru, and Sirish L. SHAH. "A Design of Multiloop Self-Tuning PID Control Systems." Transactions of the Institute of Systems, Control and Information Engineers 11, no. 4 (1998): 163–71. http://dx.doi.org/10.5687/iscie.11.163.

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10

Martin, P., and R. Katebi. "Multivariable PID tuning of dynamic ship positioning control systems." Journal of Marine Engineering & Technology 4, no. 2 (January 2005): 11–24. http://dx.doi.org/10.1080/20464177.2005.11020190.

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11

Tan, Wen, Jizhen Liu, Tongwen Chen, and Horacio J. Marquez. "ROBUST ANALYSIS AND PID TUNING OF CASCADE CONTROL SYSTEMS." Chemical Engineering Communications 192, no. 9 (September 2005): 1204–20. http://dx.doi.org/10.1080/009864490515667.

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12

Arrieta, Orlando, Ramon Vilanova, and Pedro Balaguer. "Procedure for Cascade Control Systems Design: Choice of Suitable PID Tunings." International Journal of Computers Communications & Control 3, no. 3 (September 1, 2008): 235. http://dx.doi.org/10.15837/ijccc.2008.3.2392.

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This paper provides an approach for the application of PID controllers within a cascade control system configuration. Based on considerations about the expected operating modes of both controllers, the tuning of both inner and outer loop controllers are selected accordingly. This fact motivates the use of a tuning that, for the secondary controller, provides a balanced set-point / load-disturbance performance. A new approach is also provided for the assimilation of the inner closed-loop transfer function to a suitable form for tuning of the outer controller. Due to the fact that this inevitably introduces unmodelled dynamics into the design of the primary controller, a robust tuning is needed.
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13

Zhang, Xue Ming, Gui Xiang Zhang, Feng Shao, and Qing Jie Yang. "Design of Compound Fuzzy Controller for Multivariable Systems." Applied Mechanics and Materials 16-19 (October 2009): 150–54. http://dx.doi.org/10.4028/www.scientific.net/amm.16-19.150.

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The PID controllers can be seen in lots of fields, but some complex control system cannot be controlled to achieve a desired performance index. A design method of the fuzzy PID controller that is based on the fuzzy tuning rules and formed by integrating two above control ideas is proposed in this paper. The design procedure about fuzzy PID control can be divided two steps: the first step is to build the fuzzy tuning rules by analysis, and to obtain the parameters of PID controller by reasoning, and then the control action can be determined by the PID control law. The simulation results and the practical control effects show that the compound fuzzy PID controller has better performance than that of the conventional PID control system and meet the practical demands.
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14

Zhang, Jianhua, and Junghui Chen. "Neural PID Control Strategy for Networked Process Control." Mathematical Problems in Engineering 2013 (2013): 1–11. http://dx.doi.org/10.1155/2013/752489.

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A new method with a two-layer hierarchy is presented based on a neural proportional-integral-derivative (PID) iterative learning method over the communication network for the closed-loop automatic tuning of a PID controller. It can enhance the performance of the well-known simple PID feedback control loop in the local field when real networked process control applied to systems with uncertain factors, such as external disturbance or randomly delayed measurements. The proposed PID iterative learning method is implemented by backpropagation neural networks whose weights are updated via minimizing tracking error entropy of closed-loop systems. The convergence in the mean square sense is analysed for closed-loop networked control systems. To demonstrate the potential applications of the proposed strategies, a pressure-tank experiment is provided to show the usefulness and effectiveness of the proposed design method in network process control systems.
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15

Pelc, Mariusz. "Self-tuning run-time reconfigurable PID controller." Archives of Control Sciences 21, no. 2 (January 1, 2011): 189–205. http://dx.doi.org/10.2478/v10170-010-0039-y.

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Self-tuning run-time reconfigurable PID controller Digital PID control algorithm is one of the most commonly used algorithms in the control systems area. This algorithm is very well known, it is simple, easily implementable in the computer control systems and most of all its operation is very predictable. Thus PID control has got well known impact on the control system behavior. However, in its simple form the controller have no reconfiguration support. In a case of the controlled system substantial changes (or the whole control environment, in the wider aspect, for example if the disturbances characteristics would change) it is not possible to make the PID controller robust enough. In this paper a new structure of digital PID controller is proposed, where the policy-based computing is used to equip the controller with the ability to adjust it's behavior according to the environmental changes. Application to the electro-oil evaporator which is a part of distillation installation is used to show the new controller structure in operation.
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16

Sabir, Mirza Muhammad, and Junaid Ali Khan. "Optimal Design of PID Controller for the Speed Control of DC Motor by Using Metaheuristic Techniques." Advances in Artificial Neural Systems 2014 (December 10, 2014): 1–8. http://dx.doi.org/10.1155/2014/126317.

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DC motors are used in numerous industrial applications like servo systems and speed control applications. For such systems, the Proportional+Integral+Derivative (PID) controller is usually the controller of choice due to its ease of implementation, ruggedness, and easy tuning. All the classical methods for PID controller design and tuning provide initial workable values for Kp, Ki, and Kd which are further manually fine-tuned for achieving desired performance. The manual fine tuning of the PID controller parameters is an arduous job which demands expertise and comprehensive knowledge of the domain. In this research work, some metaheuristic algorithms are explored for designing PID controller and a comprehensive comparison is made between these algorithms and classical techniques as well for the purpose of selecting the best technique for PID controller design and parameters tuning.
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17

Prokop, Roman, Zdenka Prokopová, and Monika Bakošová. "PID Control of Unstable Time-Delay Systems: Tuning and Robustness." IFAC Proceedings Volumes 33, no. 13 (June 2000): 415–20. http://dx.doi.org/10.1016/s1474-6670(17)37225-7.

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18

Tan, K. K., T. H. Lee, and X. Jiang. "Robust on-line relay automatic tuning of PID control systems." ISA Transactions 39, no. 2 (April 2000): 219–32. http://dx.doi.org/10.1016/s0019-0578(99)00050-6.

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19

Tamura, Kenichi, and Hiromitsu Ohmori. "AUTO-TUNING METHOD OF EXPANDED PID CONTROL FOR MIMO SYSTEMS." IFAC Proceedings Volumes 40, no. 13 (2007): 98–103. http://dx.doi.org/10.3182/20070829-3-ru-4911.00015.

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20

Liu, G. P., S. Daley, and G. R. Duan. "APPLICATION OF OPTIMAL-TUNING PID CONTROL TO INDUSTRIAL HYDRAULIC SYSTEMS." IFAC Proceedings Volumes 35, no. 1 (2002): 179–84. http://dx.doi.org/10.3182/20020721-6-es-1901.01181.

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21

Cetin, Meric, and Serdar Iplikci. "A novel auto-tuning PID control mechanism for nonlinear systems." ISA Transactions 58 (September 2015): 292–308. http://dx.doi.org/10.1016/j.isatra.2015.05.017.

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22

Khodja, Mohammed Abdallah, Cherif Larbes, Naeem Ramzan, and Anwar Hassan Ibrahim. "Implementation of Heuristical PID Tuning for Nonlinear System Control." International Review of Automatic Control (IREACO) 12, no. 2 (March 31, 2019): 108. http://dx.doi.org/10.15866/ireaco.v12i2.16791.

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23

Schiavo, Michele, Fabrizio Padula, Nicola Latronico, Luca Merigo, Massimiliano Paltenghi, and Antonio Visioli. "First experiments of anesthesia control with optimized PID tuning." IFAC-PapersOnLine 53, no. 2 (2020): 16125–30. http://dx.doi.org/10.1016/j.ifacol.2020.12.434.

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24

Qiao, Yujing, and Yuqi Fan. "A PID Tuning Strategy Based on a Variable Weight Beetle Antennae Search Algorithm for Hydraulic Systems." Advances in Materials Science and Engineering 2021 (September 14, 2021): 1–19. http://dx.doi.org/10.1155/2021/9579453.

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To select reasonable PID controller parameters and improve control performances of hydraulic systems, a variable weight beetle antenna search algorithm is proposed for PID tuning in the hydraulic system. The beetle antennae search algorithm is inspired by the beetle preying habit depending on symmetry antennae on the head. The proposed algorithm added the exponential equation mechanism strategy in the basic algorithm to further improve the searching performance, the convergence speed, and the optimization accuracy and obtain new iteration and an updating method in the global searching and local searching stages. In the PID tuning process, advantages of less parameters and fast iteration are realized in the PID tuning process. In this paper, different dimension functions were tested, and results calculated by the proposed algorithm were compared with other famous algorithms, and the numerical analysis was carried out, including the iteration, the box-plot, and the searching path, which comprehensively showed the searching balance in the proposed algorithm. Finally, the reasonable PID controller parameters are found by using the proposed method, and the tuned PID controller is introduced into the hydraulic system for control, and the time-domain response characteristics and frequency response characteristics are given. The results show that the proposed PID tuning method has good PID parameter tuning ability, and the tuned PID has a good control ability, which makes the hydraulic system achieve the desired effect.
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25

YUSOF, RUBIYAH, and SIGERU OMATU. "A multivariable self-tuning PID controller." International Journal of Control 57, no. 6 (June 1993): 1387–403. http://dx.doi.org/10.1080/00207179308934453.

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26

Tan, W., P. K. S. Tam, and J. Liu. "PID tuning based on loop-shaping H∞ control." IEE Proceedings - Control Theory and Applications 145, no. 6 (November 1, 1998): 485–90. http://dx.doi.org/10.1049/ip-cta:19982407.

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27

Tang, Wei, Qing-Guo Wang, Zhen Ye, and Zhiping Zhang. "PID TUNING FOR DOMINANT POLES AND PHASE MARGIN." Asian Journal of Control 9, no. 4 (May 25, 2010): 466–69. http://dx.doi.org/10.1111/j.1934-6093.2007.tb00435.x.

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28

Patel, Pritesh, and S. Janardhanan. "Near Optimal PID Controller Tuning: Interval Arithmetic Approach." IFAC-PapersOnLine 53, no. 1 (2020): 246–51. http://dx.doi.org/10.1016/j.ifacol.2020.06.042.

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29

RAJA, M., Kartikay SINGH, Aishwerya SINGH, and Ayush GUPTA. "Design of Satellite Attitude Control Systems using Adaptive Neural Networks." INCAS BULLETIN 12, no. 3 (September 1, 2020): 173–82. http://dx.doi.org/10.13111/2066-8201.2020.12.3.14.

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This paper investigates the performance of adaptive neural networks through simulations for satellite systems involving three-axis attitude control algorithms. PID tuning is the method employed traditionally. An optimally tuned, to minimizes the deviation from set point. It also responds quickly to the disturbances with some minimal overshoot. However, the disadvantage of poor performance has been observed in these controllers when manual tuning is used which in itself a monotonous process is. The PID controller using Ziegler-Nichols has more transient responses of satellite such as Overshoot, Settling time, and Steady state errors. For overcome this technique, the proposed analysis implemented an Adaptive Neural Network with PID tuning. The paper aims to combine two feedback methods by using neural networks. These methods are feed- forward and error feedback adaptive control. The research work is expected to reveal the inside working of these neural network controllers for state and error feedback input states. An error driven adaptive control systems is produced, when the neural networks acquire the knowledge of slopes and gains regarding the error feedback, while, with state feedback the system will keep trying to approximate a stable approach in order to stabilize the attitude of the satellite.
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30

Iswanto, Iswanto, Oyas Wahyunggoro, and Adha Imam Cahyadi. "Hover Position of Quadrotor Based on PD-like Fuzzy Linear Programming." International Journal of Electrical and Computer Engineering (IJECE) 6, no. 5 (October 1, 2016): 2251. http://dx.doi.org/10.11591/ijece.v6i5.9188.

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<p>The purpose of this paper is to present the altitude control algorithm for quadrotor to be able to fly at a particular altitude. Several previous researchers have conducted studies on quadrotor altitude by using PID control but there are problems in the overshoot and oscillation. To optimize the control, tunning on PID algorithm must be first conducted to determine proportional and derivative constants. Hence, the paper presents altitude control modification by using PID-like fuzzy without tuning. The PID algorithm is a control algorithm for linear systems. While, system to be controlled is a non-linear, so that linearization is needed by using equilibrium. The proposed algorithm is a modification of the PID algorithm used as an altitude control which enables quadrotor to be stable when hovering. The algorithm used is not PID algorithm with tuning using fuzzy, but this is a single input single output (SISO) control PID-like fuzzy linear programming. The result of the research shows that quadrotor can hover in a rapid raise time, steady state and settling time without performing overshoot and oscillation.</p>
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31

Iswanto, Iswanto, Oyas Wahyunggoro, and Adha Imam Cahyadi. "Hover Position of Quadrotor Based on PD-like Fuzzy Linear Programming." International Journal of Electrical and Computer Engineering (IJECE) 6, no. 5 (October 1, 2016): 2251. http://dx.doi.org/10.11591/ijece.v6i5.pp2251-2261.

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<p>The purpose of this paper is to present the altitude control algorithm for quadrotor to be able to fly at a particular altitude. Several previous researchers have conducted studies on quadrotor altitude by using PID control but there are problems in the overshoot and oscillation. To optimize the control, tunning on PID algorithm must be first conducted to determine proportional and derivative constants. Hence, the paper presents altitude control modification by using PID-like fuzzy without tuning. The PID algorithm is a control algorithm for linear systems. While, system to be controlled is a non-linear, so that linearization is needed by using equilibrium. The proposed algorithm is a modification of the PID algorithm used as an altitude control which enables quadrotor to be stable when hovering. The algorithm used is not PID algorithm with tuning using fuzzy, but this is a single input single output (SISO) control PID-like fuzzy linear programming. The result of the research shows that quadrotor can hover in a rapid raise time, steady state and settling time without performing overshoot and oscillation.</p>
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32

Karimi, A., D. Garcia, and R. Longchamp. "PID controller tuning using Bode's integrals." IEEE Transactions on Control Systems Technology 11, no. 6 (November 2003): 812–21. http://dx.doi.org/10.1109/tcst.2003.815541.

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33

Fišer, Jaromír, and Pavel Zítek. "PID Controller Tuning via Dominant Pole Placement in Comparison with Ziegler-Nichols Tuning." IFAC-PapersOnLine 52, no. 18 (2019): 43–48. http://dx.doi.org/10.1016/j.ifacol.2019.12.204.

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34

Zhang, Feng, Ye Hui Lu, Feng Qiao, and Chong Chong Bai. "Self-Tuning Fuzzy Adaptive PID Pitch Control of Wind Power Systems." Applied Mechanics and Materials 394 (September 2013): 404–9. http://dx.doi.org/10.4028/www.scientific.net/amm.394.404.

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A large variable speed constant frequency (VSCF) wind power system usually adopts the variable pitch control technology to ensure the output power is steady to ensure the safety of the wind power system above the rated wind speed. But the strong nonlinear and large moment of inertia of wind turbine result in the difficulty of variable pitch control, both simple fuzzy control and conventional PID control can not achieve a good control effect. Concerning this issue, variable pitch control algorithm is proposed based on self-tuning fuzzy adaptive PID control strategy. According to the dynamic model of VSCF, a simulation model of wind turbine control system is built in this paper with Matlab/Simulink. When the wind speed is random variable above the rated speed, the simulation results show that the proposed control strategy can keep the output power of the system around the rated power.
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35

Rad, A. Besharati, and P. J. Gawthrop. "Explicit PID Self Tuning Control for Systems with Unknown Time Delay." IFAC Proceedings Volumes 24, no. 1 (January 1991): 251–57. http://dx.doi.org/10.1016/s1474-6670(17)51328-2.

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36

Zhao, Y. M., W. F. Xie, and X. W. Tu. "Performance-based parameter tuning method of model-driven PID control systems." ISA Transactions 51, no. 3 (May 2012): 393–99. http://dx.doi.org/10.1016/j.isatra.2012.02.005.

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37

Wang, L., and W. R. Cluett. "Tuning PID controllers for integrating processes." IEE Proceedings - Control Theory and Applications 144, no. 5 (September 1, 1997): 385–92. http://dx.doi.org/10.1049/ip-cta:19971435.

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38

Kurniawan, Edi. "Analysis and Simulation of Proportional Derivative and Proportional Integral Derivative Control Systems Using Xcos Scilab." Journal of Technomaterials Physics 3, no. 1 (February 26, 2021): 36–44. http://dx.doi.org/10.32734/jotp.v3i1.5544.

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PID (Proportional Integral Derivative) control is a popular control in the industry and aims to improve the performance of a system. This control has controlling parameters, namely Kp, Ki, and Kd which will have a control effect on the overall system response. In this research, P, PD, and PID control simulations with the transfer function of the mass-damper spring as a plant using Xcos Scilab. The method used is the trial and error method by setting and varying the values of the control constants Kp, Ki, and Kd to produce the desired system response. The value adjustment of system control parameters is carried out with several variations, namely Kp control variation, Kp variation to constant Kd, Kd variation to constant Kp, Kp variation to Ki, constant Kd, variation of Ki to Kp, constant Kd and variation of Kd to Kp, Ki constant. The second method is automatic tuning which is done through mathematical calculations to obtain PID control constants, namely Zieglar Nichols PID tuning with the oscillation method. From the system simulation results, the best parameter is obtained through the Zieglar Nichols PID tuning process based on the results of the transient response analysis, namely when the proportional gain value (Kp) is 50. The system performance characteristics produced in the tuning process are 3.994 seconds of settling time at 2.36 seconds research time. resulting in a maximum overshoot value of 3.6% and a peaktime value of 3.994 seconds
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39

Merigo, Luca, Fabrizio Padula, Nicola Latronico, Teresa Mendonça, Massimiliano Paltenghi, Paula Rocha, and Antonio Visioli. "Optimized PID tuning for the automatic control of neuromuscular blockade." IFAC-PapersOnLine 51, no. 4 (2018): 66–71. http://dx.doi.org/10.1016/j.ifacol.2018.06.032.

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40

Glickman, S., R. Kulessky, and G. Nudelman. "Identification-Based PID Control Tuning for Power Station Processes." IEEE Transactions on Control Systems Technology 12, no. 1 (January 2004): 123–32. http://dx.doi.org/10.1109/tcst.2003.821955.

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41

Yusof, Rubiyah, Sigeru Omatu, and Marzuki Khalid. "Self-tuning PID control: A multivariable derivation and application." Automatica 30, no. 12 (December 1994): 1975–81. http://dx.doi.org/10.1016/0005-1098(94)90059-0.

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42

Chaikovskii, M. M., and I. B. Yadykin. "Optimal tuning of PID controllers for MIMO bilinear plants." Automation and Remote Control 70, no. 1 (January 2009): 118–32. http://dx.doi.org/10.1134/s0005117909010093.

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43

Tan, Wen, Tongwen Chen, and Horacio J. Marquez. "Robust Controller Design And Pid Tuning For Multivariable Processes." Asian Journal of Control 4, no. 4 (October 22, 2008): 439–51. http://dx.doi.org/10.1111/j.1934-6093.2002.tb00085.x.

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44

Katayama, Masaru, Toru Yamamoto, and Yasuhiro Mada. "A Design Of Multiloop Predictive Self-Tuning Pid Controllers." Asian Journal of Control 4, no. 4 (October 22, 2008): 472–81. http://dx.doi.org/10.1111/j.1934-6093.2002.tb00088.x.

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45

Makarem, Sarah, Bülent Delibas, and Burhanettin Koc. "Data-Driven Tuning of PID Controlled Piezoelectric Ultrasonic Motor." Actuators 10, no. 7 (June 29, 2021): 148. http://dx.doi.org/10.3390/act10070148.

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Ultrasonic motors employ resonance to amplify the vibrations of piezoelectric actuator, offering precise positioning and relatively long travel distances and making them ideal for robotic, optical, metrology and medical applications. As operating in resonance and force transfer through friction lead to nonlinear characteristics like creep and hysteresis, it is difficult to apply model-based control, so data-driven control offers a good alternative. Data-driven techniques are used here for iterative feedback tuning of a proportional integral derivative (PID) controller parameters and comparing between different motor driving techniques, single source and dual source dual frequency (DSDF). The controller and stage system used are both produced by the company Physik Instrumente GmbH, where a PID controller is tuned with the help of four search methods: grid search, Luus–Jaakola method, genetic algorithm, and a new hybrid method developed that combines elements of grid search and Luus–Jaakola method. The latter method was found to be quick to converge and produced consistent result, similar to the Luus–Jaakola method. Genetic Algorithm was much slower and produced sub optimal results. The grid search has also proven the DSDF driving method to be robust, less parameter dependent, and produces far less integral position error than the single source driving method.
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46

Rashid, Yasir G., and Ahmed Mohammed Abdul Hussain. "Implementing optimization of PID controller for DC motor speed control." Indonesian Journal of Electrical Engineering and Computer Science 23, no. 2 (August 1, 2021): 657. http://dx.doi.org/10.11591/ijeecs.v23.i2.pp657-664.

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The point of this paper presents an optimization technique which is flexible and quick tuning by using a genetic algorithm (GA) to obtain the optimum proportional-integral-derivative (PID) parameters for speed control of aseparately excited DC motor as a benchmark for performance analysis. The optimization method is used for searching for the proper value of PID parameters. The speed controller of DC motor using PID tuning method sincludes three types: MATALB PID tunner app., modified Ziegler-Nicholsmethod and genetic algorithm (GA). PID controller parameters (Kp, Ki and Kd) will be obtained by GA to produce optimal performance for the DC motor control system. Simulation results indicate that the tuning method of PID by using a genetic algorithm is shown to create the finest result in system performance such as settling time, rise time, percentage of overshoot and steady state error. The MATLAB/Simulink software is used to model and simulate the proposed DC motor controller system.
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47

Kuantama, Endrowednes, Tiberiu Vesselenyi, Simona Dzitac, and Radu Tarca. "PID and Fuzzy-PID Control Model for Quadcopter Attitude with Disturbance Parameter." International Journal of Computers Communications & Control 12, no. 4 (June 29, 2017): 519. http://dx.doi.org/10.15837/ijccc.2017.4.2962.

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This paper aims to present data analysis of quadcopter dynamic attitude on a circular trajectory, specifically by comparing the modeling results of conventional Proportional Integral Derivative (PID) and Fuzzy-PID controllers. Simulations of attitude stability with both control systems were done using Simulink toolbox from Matlab so the identification of each control system is clearly seen. Each control system algorithm related to roll and pitch angles which affects the horizontal movement on a circular trajectory is explained in detail. The outcome of each tuning variable of both control systems on the output movement is observable while the error magnitude can be compared with the reference angles. To obtain a deeper analysis, wind disturbance on each axis was added to the model, thus differences between each control system are more recognizable. According to simulation results, the Fuzzy-PID controller has relatively smaller errors than the PID controller and has a better capability to reject disturbances. The scaling factors of gain values of the two controllers also play a vital role in their design.
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48

Tufa, Lemma Dendena, and Marappagounder Ramasamy. "Robust and Effective PID Controller Identification for Delay Dominant Systems." Applied Mechanics and Materials 625 (September 2014): 478–81. http://dx.doi.org/10.4028/www.scientific.net/amm.625.478.

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A novel PID controller identification method based on internal model control structure is proposed. The proposed method avoids the necessity of approximating the time delay for designing the PID controller. It results in a robust and effective PID controller tuning. The method is effective for both time constant and time delay dominant systems, with much improved performance for the latter case.
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49

Maghfiroh, Hari, Muhammad Nizam, and Supriyanto Praptodiyono. "PID optimal control to reduce energy consumption in DC-drive system." International Journal of Power Electronics and Drive Systems (IJPEDS) 11, no. 4 (December 1, 2020): 2164. http://dx.doi.org/10.11591/ijpeds.v11.i4.pp2164-2172.

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<span lang="EN-US">The control system that is widely used in industry is PID (Proportional Integral Derivative). Almost 90% of industries still use PID control systems because of its simplicity, applicability, and reliability. However, the weakness of PID is that it takes a long time to tune. PID control with good performance and low energy consumption can be achieved using GA tuning with the appropriate objective function. The contribution of this paper is to propose the implementation of LQR control in the form of PID using GA tuning with LQR objective function. The proposed algorithm was implemented both in the simulation and hardware which is a mini conveyor with a DC motor. The result shows that the proposed algorithm is better in both IAE and energy consumption compared with other PID tuning, Ziegler–Nichols (ZN), and GA with IAE objective function. Compared with PID ZN, it has IAE and energy reduction by 2.76% and 16.07% respectively. Although its performance is lower than the LQR, it has other advantages that use fewer sensors. The other advantage of the proposed method is, PID is more familiar using. Therefore, it easy to be implemented in the existing system without a lot of changes.</span>
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50

Chu, Pengzi, Yi Yu, Danyang Dong, Hui Lin, and Jianjun Yuan. "NSGA-II-Based Parameter Tuning Method and GM(1,1)-Based Development of Fuzzy Immune PID Controller for Automatic Train Operation System." Mathematical Problems in Engineering 2020 (March 24, 2020): 1–20. http://dx.doi.org/10.1155/2020/3731749.

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Automatic train operation (ATO) system is one of the important components in advanced train operation control systems. Ideal controllers are expected for the automatic driving function of ATO systems. Aiming at the intelligence requirements of the systems, an NSGA-II-based parameter tuning method for the fuzzy immune PID (FI-PID) controller and a grey model GM(1,1)-based fuzzy grey immune PID (FGI-PID) controller were proposed. Taking a maglev train’s model as the control object and a velocity-time curve as the input, the feasibility of the parameter tuning method for the FI-PID controller and the applicability of the FI-PID controller and the FGI-PID controller for the ATO system were tested. The results showed that the optimized parameters were ideal, the two controllers all showed good performance on the indicators of traceability and comfort level, and the FGI-PID controller performed better than the FI-PID controller. The results exhibited the effectiveness of the proposed methods.
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