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PID control is a popular controlling technique in high accuracy control system. PID tuning is a very important stage and affects the reliability of the PID control system. This stage plays a role in determining KP,KI, and KD constants. Currently it has been a lot of PID tuning techniques that have been developed from the Ziegler-Nichols methods. PID Tuning using Internal Model Controller (IMC) by Tustin discrete approached models was used in this study. Open-loop method was used with two variation value of PWM (20% and 80%). The purposes of this study were to determine the PID constants and test those performances using a DC motor. The result of PID tuning process generated two pairs of KP, KI, and KD constants. The first were 0.4013; 0.0988; 0.0176, and the second were 0.2314; 0.0531; 0.044, respectively. The testing results with DC motor showed the performance of the both pairs of PID constants obtained were reliable enough to control motor speed that was characterized by the ability to follow the set-point value that was given and there was no steady state error. There was oscillation at 1500 rpm and 2000 rpm and motor power couldn’t achieve the set-point at 2000 rpm.

Kontrol PID merupakan salah satu teknik pengontrolan yang populer untuk pengontrolan sistem dengan ketelitian tinggi. Terdapat satu tahapan yang sangat penting dan mempengaruhi kehandalan dari sistem
kontrol PID yang dihasilkan. Tahapan tersebut adalah penalaan (tuning) PID. Tahapan ini menjadi penting karena berperan dalam penentuan konstanta PID (KP, KI, dan KD). Saat ini telah banyak teknik penalaan
PID yang telah dikembangkan dari teknik Ziegler-Nichols. Penalaan PID dengan teknik Internal Model Controller (IMC) melalui pendekatan model discrete Tustin digunakan dalam penelitian ini. Metode openloop
dengan teknik pengontrolan PWM dipakai dengan dua variasi nilai PWM yaitu 20% dan 80%. Tujuan penelitian ini adalah menemukan konstanta PID dan menguji performanya dengan motor DC. Dari proses penalaan PID yang dilakukan, diperoleh dua pasang konstanta KP, KI, dan KD. Konstanta pertama masingmasing sebesar 0.4013; 0.0988; dan 0.0176, dan pasangan kedua masing-masing sebesar 0.2314; 0.0531; dan 0.044. Hasil pengujian dengan motor DC memperlihatkan performa konstanta PID yang diperoleh cukup handal dalam mengontrol kecepatan motor yang ditandai oleh kemampuan motor dalam mengikuti nilai set-point yang diberikan dan tidak terjadi steady state error. Akan tetapi terjadi osilasi pada set-point 1500 rpm dan 2000 rpm dan kekuatan motor tidak dapat mencapai set-point 2000 rpm.


IMC PID constant PID tuning Tustin model.

Article Details

Author Biographies

Abdul Azis, 1. Institut Pertanian Bogor. 2. Universitas Hasanudin

Program Studi Ilmu Keteknikan Pertanian, Sekolah Pasca Sarjana, Institut Pertanian Bogor.
Jurusan Teknik Pertanian, Fakultas Pertanian, Universitas Hasanudin

Radite Praeko Agus Setiawan, Institut Pertanian Bogor

Departemen Teknik Mesin dan Biosistem

Wawan Hermawan, Institut Pertanian Bogor

Departemen Teknik Mesin dan Biosistem

Tineke Mandang, Institut Pertanian Bogor

Departemen Teknik Mesin dan Biosistem


  1. Ahn, K.K., D.Q. Truong. 2009. Online tuning fuzzy PID controller using robust extended Kalman filter. Journal of Process Control Vol. 19(6): 1011-
  2. 1023. doi:10.1016/j.jprocont.2009.01.005.
  3. Ang, K.H., G. Chong, Y. Li. 2005. PID control system analysis, design, and technology. IEEE transactions on control systems technology
  4. Vol.13(4): 559-576.
  5. Begum, K.G., T. Radhakrishnan, A.S. Rao, M. Chidambaram. 2016. IMC based PID Controller Tuning of Series Cascade Unstable Systems.
  6. IFAC-Papers On Line 49(1): 795-800.
  7. Bi, Q., W.J. Cai, Q.G. Wang, C.C. Hang, E.L. Lee, Y. Sun, K.D. Liu, Y. Zhang, B. Zou. 2000. Advanced controller auto-tuning and its application in HVAC systems. Control Engineering Practice Vol. 8(6):633-644.
  8. Bolton, W. 2004. Instrumentation and Control System. Di dalam: Astranto S. Sistem Instrumentasi dan Sistem Kontrol. Penerbit Erlangga, Jakarta. Hal:103 -122.
  9. Chiha, I., N. Liouane, P. Borne. 2012. Tuning PID Controller Using Multiobjective Ant Colony Optimization. Applied Computational Intelligence and Soft Computing 2012:1-7.doi:10.1155/2012/536326
  10. Donghai, L., X. Yali, W. Weijie, S. Li. 2014. Decentralized PID Controller Tuning Based on Desired Dynamic Equations. IFAC Proceedings
  11. Volumes 47(3): 5802-5807.
  12. Duka, A.V., M. Dulău, S.E. Oltean. 2016. IMC Based PID Control of a Magnetic Levitation System. Procedia Technology Vol. 22: 592-599.
  13. doi:10.1016/j.protcy.2016.01.125
  14. Eriksson, L.M., M. Johansson. 2007. PID controller tuning rules for varying time-delay systems. IEEE 2007 American Control Conference. Hal: 619-625.
  15. Franklin, G.F., J.D. Powell, A. Emami-Naeini, J.D. Powell. 1994. Feedback Control of Dynamic Systems. Addison-Wesley Reading, Boston.
  16. Kumar, G.S., D. Jayaraj, A.R. Kishan. 2010. PSO based tuning of a PID controller for a high performance drilling machine. International Journal of Computer Applications Vol.1(19):12-18.
  17. Hang, C.C., K.J. Astrom, W.K. Ho. 1991. Refinements of the Ziegler-Nichols tuning formula. IEEE Proceedings D-Control Theory and Applications.
  18. Hal.: 111-118.
  19. Ho, W.K., K.W. Lim, W. Xu. 1998. Optimal gain and phase margin tuning for PID controllers. Automatica Vol. 34(8):1009-1014.
  20. Howell, M.N., M.C Best. 2000. On-line PID tuning for engine idle-speed control using continuous action reinforcement learning automata. Control
  21. Engineering Practice Vol.8(2):147-154.
  22. Jantzen, J. 1998. Tuning of fuzzy PID controllers. Technical University of Denmark, report.
  23. Jun, M., M.G. Safonov. 1999. Automatic PID tuning: An application of unfalsified control. Proc. IEEE CCA/CACSD 2:328-333.
  24. Kadu, C.B., C.Y. Patil. 2016. Design and Implementation of Stable PID Controller for Interacting Level Control System. Procedia
  25. Computer Science Vol. 79:737-746. doi:10.1016/j. procs.2016.03.097
  26. Kao, C.C., C.W. Chuang, R.F. Fung. 2006. The selftuning PID control in a slider–crank mechanism system by applying particle swarm optimization
  27. approach. Mechatronics Vol.16(8):513-522. doi:10.1016/j.mechatronics.2006.03.007
  28. Karimi, A., D. Garcia, R. Longchamp. 2003. PID controller tuning using Bode's integrals. IEEE Transactions on Control Systems Technology Vol.
  29. 11(6):812-821.
  30. Kaya, I. 2004. IMC based automatic tuning method for PID controllers in a Smith predictor configuration. Computers & Chemical
  31. Engineering Vol. 28(3):281-290. doi:10.1016/j. compchemeng.2003.01.001
  32. Kazemian, H.B. 2001. Comparative study of a learning fuzzy PID controller and a self-tuning controller. ISA transactions Vol. 40(3):245-253.
  33. Killingsworth, N.J., M. Krstic. 2006. PID tuning using extremum seeking: online, model-free performance optimization. IEEE control systems
  34. Vol. 26(1):70-79.
  35. Kim, D.H., W.P. Hong, J.I. Park. 2002. Auto-tuning of reference model based PID controller using immune algorithm. Proceedings of the 2002
  36. Congress on Evolutionary Computation Hal.: 483-488.
  37. Kim, J.S., J.H. Kim, J.M. Park, S.M. Park, W.Y. Choe, H. Heo. 2008. Auto tuning PID controller based on improved genetic algorithm for reverse osmosis plant. World Academy of Science, Engineering
  38. and Technology Vol. 47: 384-389.
  39. Kumar, S.G., R. Jain, N. Anantharaman, V. Dharmalingam, K. Begum. 2008. Genetic algorithm based PID controller tuning for a
  40. model bioreactor. Indian chemical engineer Vol.50(3):214-226.
  41. Lee, C.H., C.C. Teng. 2003. Calculation of PID controller parameters by using a fuzzy neural network. ISA Transactions Vol.42(3): 391-400.
  42. doi:10.1016/s0019-0578(07)60142-6
  43. Li, X.F., G. Chen, Y.G. Wang. 2016a. IMC-PID controller design for power control loop based on closed-loop identification in the frequency
  44. domain. IFAC-Papers On Line Vol. 49(4):79-84.
  45. Li, X.F., D.J. Ding, Y.G. Wang, Z. Huang. 2016b. Cascade IMC-PID control of superheated steam temperature based on closed-loop identification
  46. in the frequency domain. IFAC-Papers On Line Vol. 49(18):91-97.
  47. Li, Y., K.H. Ang, G.C. Chong. 2006. PID control system analysis and design. IEEE Control Systems Vol. 26(1):32-41.
  48. Lin, F., R.D. Brandt, G. Saikalis. 2000. Self-tuning of PID controllers by adaptive interaction. Proceedings of the American Control Conference.
  49. Hal.: 3676-3681.
  50. Martins, F.G. 2005. Tuning PID controllers using the ITAE criterion. International Journal of Engineering Education Vol. 21(5):867.
  51. Nhon, P., I. Elamvazuthi, H. Fayek, S. Parasuraman, M.A. Khan. 2014. Intelligent control of rehabilitation robot: Auto tuning PID controller with interval type 2 Fuzzy for DC servomotor. Procedia Computer
  52. Science Vol. 42:183-190.
  53. Radite, P.A.S., W. Hermawan, A. Azis, B. Budiyanto. 2011. Design and performance test of embedded module metering device for variable rate fertilizer applicator. Proc. of ICORAS Int’l Conference on Robotic Automation Sistem, Terengganu Malaysia; paper E-145: page 149-153.
  54. Radite, P.A.S., M.T. Sapsal, W. Hermawan, B. Budiyanto. 2012. Variable rate Fertilizer applicator based on AVR microcontroller. Proceeding of
  55. AFITA/WCCA (20)-02; 2012; Taipei, Taiwan. Hal:141
  56. Shahrokhi, M., A. Zomorrodi. 2013. Comparison of PID controller tuning methods. Department of Chemical & Petroleum Engineering, Sharif
  57. University of Technology.
  58. Tan, W., J. Liu, P. Tam. 1998. PID tuning based on loop-shaping H∞ control. IEE Proceedings-Control Theory and Applications Vol. 145(6):485-490.
  59. Varol, H.A., Z. Bingul. 2004. A new PID tuning technique using ant algorithm. Proceedings of the 2004 American Control Conference. Hal.: 2154-2159.
  60. Zhang, J., J. Zhuang, H. Du, S.A. Wang. 2009. Self-organizing genetic algorithm based tuning of PID controllers. Information Sciences Vol.
  61. 179(7):1007-1018. doi:10.1016/j.ins.2008.11.038