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Sensors & Transducers Journal, Vol. 109, Issue 10, October 2009, pp. 43-58Table III. Variation in Adaptation Gains for the Different Controllers.Model K P K I θ1θ2Max Min Max Min Min Max MinM 8 (s) & M 11 (s) 0.017 -0.017 1.4825 1.481 0.83 0.7 1.16 1.03M 7 (s) & M 12 (s) 0.015 -0.014 1.4765 1.4705 0.25 - 0.05 1.61 .25M 6 (s) & M 13 (s) 0.016 -0.016 1.467 1.461 0.52 0.29 1.39 .15M 5 (s) & M 14 (s) 0.007 -0.016 1.4583 1.4563 0.74 0.55 1.27 .07M4(s) & M15(s) 0.015 -0.015 1.437 1.432 0.83 0.7 1.16 .03M3(s) & M16(s) 0.002 -0.002 1.4437 1.44 0.91 0.82 1.07 .98M2(s) & M17(s) 0.016 -0.014 1.63 1.54 2.4 0.09 1.1 -0.3Table IV. Time Integral Performance Criteria (Simulation).ModelAdaptive MITAdaptive PIISE IAE ITAE ISE IAE ITAEM 1 (s) 54.15 19.22 184.6 43.56 17.86 284.3M 2 (s) 38.6 15.91 173.1 32.69 15.55 258.1M 3 (s) 22.49 11.85 158.9 20.23 11.64 199.4M 4 (s) 13.02 0.942 180.5 11.81 8.457 156.1M 7 (s) 9.392 13.27 410.9 7.742 7.006 143.8M 15 (s) 12.95 10.49 207.6 11.2 8.082 149.2M 16 (s) 23.15 12 .0 157.5 20.16 11.6 198.6M 17 (s) 28.82 13.18 163.2 26.45 12.94 204.6M 18 (s) 55.15 19.55 186.4 44.04 18.03 288.3Table V. Time Integral Performance Criteria for a Stabilized System (Simulation- Model M7).Controller ISE IAE ITAEAdaptive MIT1 18.42 12.43 -246.1Adaptive PI 14.06 10.88 -72.53Flow Rate (lpm)Table VI. Time Integral Performance Criteria (Real Time).Adaptive MITAdaptive PIISE IAE ITAE ISE IAE ITAE575 – 725 & 725 – 575 0.0064 0.5541 85.16 0.00105 0.2358 23.91025 – 1170 & 1170 – 1025 0.6406 10.05 121.0 0.1056 1.916 109.9872 – 1025 & 1025 – 872 0.09036 3.226 401.1 0.0044 0.6084 68.98725 – 872 & 872 – 725 0.02113 1.017 97.52 0.0037 0.5133 48.9428 – 575 & 575 – 428 0.0083 0.572 61.81 0.00819 0.5767 58.61280 – 428 & 428 – 280 0.0048 0.4898 46.26 0.0054 0.6611 91.07134 – 280 & 280 – 134 0.05178 2.208 229.5 0.0402 1.706 23.956
Sensors & Transducers Journal, Vol. 109, Issue 10, October 2009, pp. 43-587. ConclusionsThis paper has presented design of adaptive PI controller based on MIT algorithm and identification ofthe nonlinear Air Flow Control System using the data acquired in real time. The methodology ofdesign and implementation of Adaptive controllers in simulation are discussed. The performance ofAdaptive PI and MIT controllers are validated in real time by interfacing the laboratory Air FlowControl System through dSPACE interface Card and MATLAB. The overall system performancewhen employing Adaptive PI is observed to be better than that of the system with Adaptive MITcontroller. The oscillations in the PI controller output are negligible when compared to MIT controller.It is inferred that there is an increase of -12 % to 95 % in ISE values, -34 % to 81 %, increase in IAEvalues for the Adaptive MIT controller from the comparison table VI. Further it is inferred that ITAEvalues of adaptive MIT controllers has an increase of -96 % to 90 % when compared to adaptive PI. Soit can be concluded that except for flow ranges from 280 – 428 & 428 – 280 lpm the adaptive PIcontroller outperforms the adaptive MIT controller. Adaptive PI controller responds with 76.33 %lesser, 87.53 % lesser IAE and 29.47 % lesser ITAE (Table V) when compared to Adaptive MIT-1controller. From the simulation and experimental responses it is found that the adaptive PI controller isa better controller for this airflow process.AcknowledgementsD. Rathikarani, author thanks the authorities of Annamalai University for granting permission toaccess the facilities in the Instrumentation and Process Control lab to carry out the experimentationwork.References[1]. B. Wayne Bequette, Process Control – Modelling, Design and Simulation, Prentice-Hall of India, 2003,pp. 132.[2]. George Stephanopoulos, Chemical Process Control, Prentice Hall of India, 1984, pp. 247.[3]. Karl Johan Astrom, PID Controllers-Theory, Design, and Tuning, ISA, North California, NC, 1994.[4]. Astrom, K. J, Theory and applications of adaptive control - A survey, Automatica, 19, 5, 1983,pp. 471–486.[5]. Clarke, D. W. and Gawthrop, P. J., Self-tuning control, Proc. Inst. Electr. Eng., 126 D, 6, 1979,pp. 633–640.[6]. Ziegler, J. G. and Nichols, N. B., Optimum settings for automatic controllers, Trans. ASME, 1942, 64, 8,pp. 759–768.[7]. Nims, P. T, Some Design Criteria for Automatic Controls, Trans. AIEE, 70, 1950, pp. 606–611.[8]. Chien, K. L., Hrones, J. A., and Reswick, J. B, On the automatic control of generalized passive systems,Trans. ASME, 74, 1972, pp. 175–185.[9]. K. Sukvichai, The Application of Nonlinear Model Reference PID Controller for a Planar Robot Arm, inProceedings of ECTI-CON, Vol.2, 2008, pp. 637-640.[10]. Xu Guo-kail, Research on Discrete Model Reference Adaptive Control System of Electric Vehicle,SICE- ICASE International Joint Conference, 2006.[11]. Astrom, K. J. and Hagglund T, Automatic tuning of PID controllers, Instrument Society of America,Research Triangle Park, 1988.[12]. Nishikawa, Y., Sannomiya, N., Ohta, T. and Tanaka, H., A method for autotuning of PID controlparameters, Automatica, 1984, 20, 3, pp. 321–332.[13]. Menani, S., and Koivo, H. N., Relay tuning of multivariable PI controllers, Preprints of 13 th World IFACCongress, San Francisco, Vol. K, 1996, pp. 139–144.[14]. N. Selvaganesan, Online Tuning of PID Controller for Time Variant Systems using Genetic Algorithm,Proceedings of the Second International Conference on ‘Artificial Intelligence in Engineering &Technology’, August 3-5, 2004, pp. 74-77.57
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