Design and Implementation of I-PD Controller for DC Motor Speed Control System by Adaptive Tabu Search

Design and Implementation of I-PD Controller for DC Motor Speed Control System by Adaptive Tabu Search

Автор: Thanet Ketthong, Satean Tunyasirut, Deacha Puangdownreong

Журнал: International Journal of Intelligent Systems and Applications(IJISA) @ijisa

Статья в выпуске: 9 vol.9, 2017 года.

Бесплатный доступ

One of the modified versions of the PID controller is the I-PD controller. It was proposed for eliminating the proportional and derivative kick appeared during set point change. In this paper, the optimal I-PD controller design for DC motor speed control system by the adaptive tabu search (ATS), one of the most efficient metaheuristic optimization techniques, is proposed. In a control system, DC motor is the principle and it is widely used because of the power from existing direct-current lighting power distribution systems. It can be controlled over a wide range and a variable supply. In this research, TMS320F28335 DSP microcontroller is implemented for DC motor speed control. This processor consists of the several peripheral circuits for motor drive application such as analog to digital, encoder digital to analog and PWM input/output interface circuits. These interface circuits can be used in both of DC and AC motor controls. For the control algorithm development and the Code Composer Studio (CCS) compiler can be used together with TMS320F28335 DSP in MATLAB/SIMULINK. This proposed method is tested with the DC motor, 1260, 1400, 1540 rpm and 24 volts, consecutively to verify the performance of the I-PD controller designed for DC motor speed control system using the speed and control signal response to many load disturbances. The simulation of DC motor is based on MATLAB/SIMULINK. The implementation results are compared with the simulation results. The correlation in the experiment shows that they are high related. In this paper show the effectiveness of the proposed methods and discuss how they could generalize to other systems by the simulation and experimentation. The results show that the I-PD parameters can be optimized by the ATS. The controlled system with I-PD provides better responses once compared to that with a basic parallel PID controller.

Еще

Adaptive Tabu Search, I-PD Controller, DC Motor Speed Control, Metaheuristic Optimization

Короткий адрес: https://sciup.org/15010968

IDR: 15010968

Список литературы Design and Implementation of I-PD Controller for DC Motor Speed Control System by Adaptive Tabu Search

  • N. Minorsky, Directional stability of automatically steered bodies. American Society of Naval Engineering, 34, 284, 1922.
  • S. Bennett, Development of the PID controller. IEEE Control System Magazine, 1955, pp. 58-65.
  • S. P. Yadav and V.K. Tripathi, A Case Study of DC Motor Speed Control with PID Controller through MAT LAB. International Journal of Advanced Research in Computer and Communication Engineering, 2016, pp.1008-1011.
  • A. Nawikavatan, S. Tunyasrirut and D. Puangdownreong, “Application of intensified current search to multiobjective PID controller optimization,” International Journal of Intelligent Systems and Applications (IJISA), Vol. 8 No.11, pp. 51–60, 2016. DOI: 10.5815/ijisa.2016.11.06.
  • Y. Lingzhi, Z. Chengdong and W. Genping, “Research of self-tuning PID for PMSM vector control based on improved KMTOA,” International Journal of Intelligent Systems and Applications (IJISA), Vol. 9 No.3, pp. 60–67, 2017. DOI: 10.5815/ijisa.2017.03.08.
  • M.A. Johnson and M.H. Moradi, PID Control: New Identification and Design Methods, Springer, London, UK, 2005.
  • T. Sato and A. Inoue, A design method of multirate I-PD controller based on multirate generalized predictive control law. SICE Annual Conference, 2004, pp.17-22.
  • T. Woo, J.S. Kim and Y.C. Kim, Digital control of UPS inverter with time response specifications. KIEE Trans., on Electrical Machinery and Energy Conversion Systems, 5-B(2), 2005, pp.196-203.
  • S. Cheol, S. Kim Choo, K. Kang Han, Y. Bae Sung, J. Cho Hyung and Park, A Design of I-PD controller using CDM. International Conference on Control, Automation and Systems, 2007, pp.477-480.
  • H. KONDO. and Y. OCHI, I-PD Flight Controller Design based on Integral-Type Optimal Servomechanism. SICE Journal of Control, Measurement, and System Integration, Vol. 3, No. 6, November 2010 pp. 448–455.
  • S. Mohizuki and H. Ichihara, I-PD controller design based on generalized KYP lemma for ball and plate system. European Control Conference (ECC), 2013, pp.2855-2860.
  • W. Chatrattanawuth, N. Suksariwattanagul, T. Benjanarasuth and J. Ngamwiwit, Fuzzy I-PD controller for level control. SICE-ICASE International Joint Conference, 2006, pp. 5649–5652.
  • D. Sandhya, R.B. Amarendra, and K.A.G. Rao, Fuzzy I-PD and fuzzy PID control of non-linear systems. International Conference on Control, Automation, Communication and Energy Conservation, 2009, pp. 1-6
  • S.J.S. Prasad, S. Varghese and P.A. Balakrishnan, Particle swarm optimized I-PD controller for second order time delayed system. International Journal of Soft Computing and Engineering (IJSCE), 2(1), 2012, pp.299-302.
  • S.J.S. Prasad and P.A. Balakrishnan, PSO based I-PD controller for barrel temperature control in plastic injection molding system. European Journal of Scientific Research, 80(3), 2012 pp.351-357.
  • V. Rajinikanth and K. Latha, I-PD controller tuning for unstable system using bacterial foraging algorithm: a study based on various error criterion. Applied Computational Intelligence and Soft Computing, 2012, pp.1–10.
  • H. Ikeda and H. Tsuyoshi, Design of m-IPD controller of multi-inertia system using differential evolution. The International Power Electronics Conference, 2014, pp. 2476–2482.
  • S. J. Suji Prasad, R. Meenakumari and P. A. Balakrishnan, Optimization of I-PD controller parameters with multi objective particle swarm optimization, Journal of Theoretical and Applied Information Technology, 20th August 2014. Vol. 66 No.2, pp. 542-546.
  • S. Sujitjorn, T. Kulworawanichpong, D. Puangdownreong and K-N. Areerak, Adaptive Tabu Search and Applications in Engineering Design, Frontiers in Artificial Intelligent and Applications, IOS Press, Amsterdam, Netherlands, 2006.
  • K.-N. Areerak, T. Kulworawanichpong and S. Sujitjorn, Moving towards a new era of intelligent protection through digital relaying in power system. Lecture Notes in Artificial Intelligence, 3215, 2004, pp.1255–1261.
  • D. Puangdownreong and S. Sujitjorn, Image approach to system identification. WSEAS Transactions on Systems, 5(5), 2006, pp. 930–938.
  • D. Puangdownreong and S. Sujitjorn, Obtaining an optimum PID controller via adaptive tabu search. Lecture Notes in Computer Science, 4432(2), 2007, 747–755.
  • D. Puangdownreong, T. Kulworawanichpong, and S. Sujitjorn, Finite Convergence and Performance Evaluation of Adaptive Tabu Search. Lecture Notes in Artificial Intelligence, Vol. 3215. Springer-Verlag, Berlin Heidelberg (2004) 710-717.
  • D. Puangdownreong, K-N. Areerak, A. Srikaew, S. Sujitjorn, and P. Totarong, System Identification via Adaptive Tabu Search. Proc. IEEE Int. Conf. on Industrial Technology (ICIT’02), Vol. 2 (2002) 915-920.
  • R. Firoozian, Servo Motors and Industrial Control Theory, Springer International Publishing Switzerland, 2014.
  • P. Vas, Parameter Estimation, Condition Monitoring and Diagnosis of Electrical Machines, Oxford University Press, 1993.
  • L. Ljung, System identification: Theory for the user, Prentice-Hall, 1987.
  • P. Eykhoff, System identification: Parameter and State Estimation, John Wiley & Sons, 1974.
  • F. Piltan, S. TayebiHaghighi and Nasri B. Sulaiman, “Comparative study between ARX and ARMAX system identification,” International Journal of Intelligent Systems and Applications (IJISA), Vol. 9 No.2, pp. 25–34, 2017. DOI: 10.5815/ijisa.2017.02.04.
  • I. J. Leontaritis and S. A. Billings, Input-output parametric models for nonlinear systems (part I: deterministic nonlinear systems), Int. J. Control, 1985, 41(2), 303 – 328.
  • I. J. Leontaritis and S. A. Billings, Input-output parametric models for nonlinear systems (part II: stochastic nonlinear systems), Int. J. Control, 1985, 41(2), 329 – 344.
  • D. Puangdownreong, S. Sujitjorn and T. Kulworawanichpong, Convergence analysis of adaptive tabu search, ScienceAsia Journal of the Science Society of Thailand, 2004, 30, 183-90.
  • S. Sujitjorn, T. Kulworawanichpong, D. Puangdownreong and K-N. Areerak, Adaptive tabu search and applications in engineering design, Frontiers in Artificial Intelligent and Applications, IOS Press, Amsterdam, Netherlands, 2006.
  • D. Puangdownreong, S. Sujitjorn, Obtaining an optimum PID controller via adaptive tabu search, Lecture Notes in Computer Science, 2007, 4432, 747 – 755.
  • D. Puangdownreong, K-N. Areerak, K-L. Areerak, T. Kulworawanichpong, and S. Sujitjorn, Application of adaptive tabu search to system identification, The 24th IASTED International Conference on Modelling, Identification, and Control (MIC2005), 2005, 178 – 183.
  • T. Ketthong, C. Kiree, S. Tunyasrirut and D. Puangdownreong, Parameter identification of DC-servo motor by adaptive tabu search. The International Annual Symposium on Computational Science and Engineering (ANSCSE19), 2015, 79–83
  • T. Ketthong, S. Tunyasrirut and D. Puangdownreong, ATS base I-PD controller optimization for DC motor speed control system, Global Engineering & Applied Science Conference, 2015, pp.91-98.
  • K. Ogata, Modern Control Engineering, Prentice Hall, New Jersey, 2010.
  • D. Puangdownreong, Current search: performance evaluation and application to dc motor speed control system design, Intelligent Control and Automation, 2013, 4(1), 42 – 54.
  • http://www.ti.com/lit/er/sprz272k/sprz272k.pdf, access on January, 2017.
  • http://www.ti.com/lit/sg/sprb176ad/sprb176ad.pdf, access on January, 2017.
Еще
Статья научная
QR-код для установки приложения SciUp Наведите камеру и скачайте бесплатное приложение SciUp