СИСТЕМЫ АНАЛИЗА И ОБРАБОТКИ ДАННЫХ

In this paper the PID controller and the Fuzzy Logic Controller (FLC) are used to control the speed of separately excited DC motors. The proportional, integral and derivate (KP, KI, KD) gains of the PID controller are adjusted according to Fuzzy Logic rules. The FLC cotroller is designed according to fuzzy rules so that the system is fundamentally robust. Twenty-five fuzzy rules for self-tuning of each parameter of the PID controller are considered. The FLC has two inputs; the first one is the motor speed error (the difference between the reference and actual speed) and the second one is a change in the speed error (speed error derivative). The output of the FLC, i.e. the parameters of the PID controller, are used to control the speed of the separately excited DC Motor. This study shows that the precisiom feature of the PID controllers and the flexibllity feature of the fuzzy controller are presented in the  fuzzy self-tuning PID controller. The fuzzy self – tuning approach implemented on the conventional PID structure improved the dynamic and static response of the system. The salient features of both conventional and fuzzy self-tuning controller outputs are explored by simulation  using MATLAB. The simulation results demonstrate that the proposed self-tuned PID controller i.plementd a good dynamic behavior of the DC motor i.e. perfect speed tracking with a settling time, minimum overshoot and minimum steady state errorws.

1. Alsayed Y.M., Maamoun A., Shaltout A. High performance control of PMSM drive system implementation based on DSP real-time controller. Proceedings of the 2019 International Conference on Innovative Trends in Computer Engineering (ITCE), Aswan, Egypt, Febraury 2019, pp. 225–230.

2. Steffi Mary S., Mohammed Shaffi J. A review paper on speed control of DC Motor & Virtual Instrumentation. International Journal of Innovative Research in Computer and Communication Engineering, 2018 vol. 6 (2), pp. 1654–1657. – DOI: 10.15680/IJIRCCE.2018.0602091.

3. Zhang J., Wang N., Wang S. A developed method of tuning PID controllers with fuzzy rules for integrating process. Proceedings of the 2004 American Control Conference, Boston, 2004, vol. 2, pp. 1109–1114.

4. Ang K.H., Chong G., Li Y. PID control system analysis, design and technology. IEEE Transactionon Control System Technology, 2005, vol. 13, no. 4, pp. 559–576.

5. Li H.X., Tso S.K. Quantitative design and analysis of fuzzy proportional-integral-derivative control – a step towards autotuning. International Journal of System Science, 2000, vol. 31, no. 5, pp. 545–553.

6. Sooraksa P., Pattaradej T., Chen G. Design and implementation of fuzzy P2ID controller for handlebar control of a bicycle robot. Integrated Computer-Aided Engineering,, 2002, vol. 9, no. 4, pp. 319–331.

7. Tang W., Chen C., Lu R. de. A modified fuzzy PI controller for a flexible-joint robot arm with uncertainties. Fuzzy Set and System, 2001, vol. 118, pp. 109–119.

8. Fan L., Meng Joo E. Design for auto-tuning PID controller based on genetic algorithms. 4th IEEE Conference on Industrial Electronics and Applications (ICIEA 2009), Xi’an, China, 2009, pp. 1924–1928. DOI: 10.1109/ICIEA.2009.5138538.

9. Sabri S.S. Optimal fuzzy controller design for (Cuk) converter circuit using genetic algorithm. M.Sc thesis. University of Mosul. Mosul, Iraq, 2008.

10. Kumar R., Girdhar V. High performance fuzzy adaptive control for D.C. motor. International Journal of Engineering Research and Technology (IJERT), 2012, vol. 1, iss. 8,

11. Namazov M., Basturk O. DC motor position control using fuzzy proportional-derivative controllers with different defuzzification methods. Turkish Journal of Fuzzy Systems, 2010, vol. 1, no. 1, pp. 36–54.

12. Bansal U.K., Narvey R. Speed control of DC motor using fuzzy PID controller. Advance in Electronic and Electric Engineering, 2010, vol. 3, no. 9, pp. 1209–1220.

13. Grimholt C., Skogestad S. Optimal PID Control of double integrating processes. IFAC PapersOnLine, 2016, vol. 49 (7), pp. 127–132.

14. Suman S.K., Giri V.K. Investigation and implementation of genetic algorithm for speed control of DC motor based on PID controller. Journal of Engineering and Applied Sciences, 2017, vol. 100 (6), pp. 1428–1433.

15. Gubara W., Elnain M., Babiker S.F. Comparative study on the speed of DC motor using PID and FLC. IEEE Conference of Basic Sciences and Engineering Studies (SGCAC). Khartoum, Sudan, 2016, pp. 24–29.

16. Suman S.K., Giri V.K. Speed control of DC motor using optimization techniques based PID controller. IEEE International Conference on Engineering and Technology (ICETECH), Coimbatore, India, 2016, pp. 581–587.

17. Pimentel J.C.G., Gad E. An algebraic approach for the stability analysis of BLDC motor controllers. arXiv:2007.01387. 2020, pp. 1–12.

18. Lotfy A., Kaveh M., Mosavi M.R., Rahmati A.R. An enhanced fuzzy controller based on improved genetic algorithm for speed control of DC motors. Analog Integrated Circuits and Signal Processing, 2020, vol. 105, pp. 141–155. DOI: 10.1007/s10470-020-01599-9.

19. Mohamed M.E.A., Yanling G., Osman B.A.r., Mohamed A.A. Design of speed control system of DC motor based on PID tuning with fuzzy define weighting point. 2019 International Conference on Computer, Control, Electrical, and Electronics Engineering (ICCCEEE), Khartoum, Sudan, 2019, pp. 1–6. DOI: 10.1109/ICCCEEE46830.2019.9071374.

20. Gupta S.K., P. Varshney. Fractional Fuzzy PID controller for speed control of DC Motor. 2013 Third International Conference on Advances in Computing and Communications, Kochi, Kerala, India, 2013, pp. 1–4. DOI: 10.1109/ICACC.2013.7.

21. Yanling G., Mohamed M.E.A. Study on the extent of the impact of data set type on the performance of ANFIS for controlling the speed of DC motor. Journal of Engineering and Technological Sciences, 2019, vol. 51, no. 1, pp. 83–102.

22. Mohamed M.E.A., Guo Y. Separately excited DC motor speed tracking control using adaptive neuro-fuzzy inference system based on genetic algorithm particle swarm optimization and fuzzy auto-tuning PID. IOP Conference Series: Earth and Environmental Science, 2019, vol. 300, no. 4, p. 042114.

23. Zhao H., Wang Y., Guo C., Luo P., Zhao D. Research on self-adaptive fuzzy speed control strategy of brushless DC motor for stronger robustness. 2019 Chinese Control And Decision Conference (CCDC), Nanchang, China, 2019, pp. 2483–2488. DOI: 10.1109/CCDC.2019.8832713.

24. Tarannum T. Intelligent speed control of DC motor using ANFIS D controller. 2019 1st International Conference on Advances in Science, Engineering and Robotics Technology (ICASERT), Dhaka, Bangladesh, 2019, pp. 1–5. DOI: 10.1109/ICASERT.2019.8934620.

25. Rauf P., Jamil M., Gilani S.O., Rind S.J. Comparison of nonlinear controllers for speed control of DC motor. 2018 IEEE 9th Annual Information Technology, Electronics and Mobile Communication Conference (IEMCON), Vancouver, BC, 2018, pp. 389–394. DOI: 10.1109/IEMCON.2018.8614853.