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Fuzzy Logic Aided PID Controller for Induction Motor Speed Control


Affiliations
1 Department of Electrical and Electronic Engineering, Chukwuemeka Odumegwu Ojukwu University, Uli, Nigeria., Nigeria
 

This paper presents design of intelligent based controller for speed control of induction motor. The proposed system integrates a classical proportional-integral-derivative (PID) controller and intelligent algorithm based on fuzzy logic control (FLC). This scheme takes advantages of classical PID and FLC to improve the speed response performance of induction motor analysed in terms of transient and steady state time domain characteristics. The FLC designed was implemented using the fuzzy block of MATLAB/Simulink based on Mamdani model and comprises 9 fuzzy variables and 49 logic (intelligent) rules that define the system behaviour. The FLC takes the loop error and its rate of change to manipulate the input command to the PID control so that the response speed signal matches with the desired speed signal resulting in reduced rise time, peak time, settling time, overshoot, and improved steady state error. The designed intelligent aided PID controller was implemented and the simulation result provided a rise of time of 0.8354 second, peak time of 4.9615 seconds, settling time of 1.2240 seconds, final value (actual speed) of 1724.9 rpm, steady state error 0.1 rpm. Simulation comparison with conventional PID controller showed that the PID yielded a rise time of 1.6723 seconds, peak time of 4.5475 seconds, peak overshoot of 0.6913 %, settling time of 2.9646 seconds, final value (actual speed) 1736.1 rpm, and steady state error of 11.1 rpm. Generally, simulation results indicated that the intelligent (FLC) aided classical PID control improve the system performance and achieved the rated speed of the motor.

Keywords

Controller, Fuzzy logic, Induction motor, Speed control, PID.
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  • A. Fattah, “Design and analysis of speed control using hybrid PID-Fuzzy controller for induction motors,” Master’s Thesis, Western Michigan University, 2015.

  • A. Kusagur, S. F. Kodad, & S. Ram, “Modelling & simulation of an ANFIS controller for an AC drive,” World Journal of Modelling and Simulation, vol. 8, No. 1, pp. 36-49, 2011.

  • P. K. Behera, M. K. Behera, & A. K. Sahoo, “Comparative analysis of scalar & vector control of induction motor through modeling & simulation,” International Journal of Innovative Research in Electrical, Electronics, Instrumentation and Control Engineering, vol. 2, No. 4, pp. 1340-1344, 2014.

  • A. A. Idoko, I. T. Thuku, S. Y. Musa, & C. Amos, “Design of tuning mechanism of PID controller for application in three phase induction motor speed control,” Int. Journal of Advanced Engg Research & Science, vol. 4, No. 11, pp. 138 – 146, 2017.

  • D. T. Korsane, A. Polke, S. K. Mude, C. S. Hiwarkar, K. Korsane, “Speed performance of three phase induction motor by using simplified vector control method,” International Journal of Engineering Research in Electrical and Electronic Engineering, vol. 4, No. 3, pp. 172 – 178, 2018.

  • Y. Sanjaya, A. Fauzi, D. Edikresnha, M. M. Munir, M. M., & Khairurijal, “Single phase induction motor speed regulation using a PID controller for rotary forcespinning apparatus,” Procedia Engineering, vol. 170, pp. 404 – 409, 2017.

  • H. Hartono, R. I. Sudjoko, & P. Iswahyudi, “Speed control of three phase induction motor using universal bridge and PID controller,” Journal of Physics: Conference Series, 1381, 012053, 2019.

  • Irianto, F. D. Murdianto, E. Sunarno, & D. D. Proboningtyas, “Comparison method of PI, PID, and fuzzy logic controller to maintain speed stability in single phase induction motors,” INTEK Jurnal Penelitian, vol. 8, No.1, pp. 7 – 16, 2021.

  • C. Chengaiah, & S. Prasad, “Performance of induction motor drive by indirect vector controlled method using PI and fuzzy controllers,” International Journal of Science and Environment, vol. 2, No. 3, pp. 475 – 469, 2013.

  • M. M. Malatji, “Derivation and implementation of a DQ model of an induction machine using MATLAB/Simulink,” Electromechanical Conversion, Technical Report, pp. 1-8.

  • A. W. Leedy,”Simulink/MATLAB dynamic induction motor model for use in undergraduate electric machines and power electronics courses,” 2013 Proceedings of IEEE Southeast Conference, Jacksonville, FL, 1-6.

  • P. O. Ugochukwu, P. C. Eze, & D. C. Oyiogu, “Enhance the performance of AVR system with prefilter aided PID controller,” Access International Journal of Research & Devel, vol. 1, No. 1, pp. 21-32.

  • P. C. Eze, A. E., Jonathan, B. C. Agwah, & E. A. Okoronkwo, “Improving the performance response of mobile satellite dish antenna network within Nigeria,” Journal of Electrical Engineering, Electronics, Control and Computer Science, vol. 6, No. 21, 25-30, 2020.

  • P. C. Eze, B. O. Ekengwu, N. C. Asiegbu, & T. I. Ozue, “Adjustable gain enhanced fuzzy logic controller for optimal wheel slip ratio tracking in hard braking control system,” Advances in Electrical and Electronic Engineering, vol. 19, No. 3, pp. 231 – 242, 2021.


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  • Fuzzy Logic Aided PID Controller for Induction Motor Speed Control

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Authors

Cosmas Anayo Okeke
Department of Electrical and Electronic Engineering, Chukwuemeka Odumegwu Ojukwu University, Uli, Nigeria., Nigeria
Innocent Ifeanyi Okonkwo
Department of Electrical and Electronic Engineering, Chukwuemeka Odumegwu Ojukwu University, Uli, Nigeria., Nigeria

Abstract


This paper presents design of intelligent based controller for speed control of induction motor. The proposed system integrates a classical proportional-integral-derivative (PID) controller and intelligent algorithm based on fuzzy logic control (FLC). This scheme takes advantages of classical PID and FLC to improve the speed response performance of induction motor analysed in terms of transient and steady state time domain characteristics. The FLC designed was implemented using the fuzzy block of MATLAB/Simulink based on Mamdani model and comprises 9 fuzzy variables and 49 logic (intelligent) rules that define the system behaviour. The FLC takes the loop error and its rate of change to manipulate the input command to the PID control so that the response speed signal matches with the desired speed signal resulting in reduced rise time, peak time, settling time, overshoot, and improved steady state error. The designed intelligent aided PID controller was implemented and the simulation result provided a rise of time of 0.8354 second, peak time of 4.9615 seconds, settling time of 1.2240 seconds, final value (actual speed) of 1724.9 rpm, steady state error 0.1 rpm. Simulation comparison with conventional PID controller showed that the PID yielded a rise time of 1.6723 seconds, peak time of 4.5475 seconds, peak overshoot of 0.6913 %, settling time of 2.9646 seconds, final value (actual speed) 1736.1 rpm, and steady state error of 11.1 rpm. Generally, simulation results indicated that the intelligent (FLC) aided classical PID control improve the system performance and achieved the rated speed of the motor.

Keywords


Controller, Fuzzy logic, Induction motor, Speed control, PID.

References