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Online Adaptive Control for Non Linear Processes Under Influence of External Disturbance
Nisha Jha, Udaibir Singh, T.K. Saxena, Avinashi Kapoor
Pages - 36 - 46     |    Revised - 01-05-2011     |    Published - 31-05-2011
Volume - 2   Issue - 2    |    Publication Date - May / June 2011  Table of Contents
Neural Network Based PID (NNPID) Controller, Temperature Controller, Back-Propagation Neural Network, Load Disturbance
In this paper a novel temperature controller, for non linear processes, under the influence of external disturbance, has been proposed. The control process has been carried out by Neural Network based Proportional, Integral and Derivative (NNPID). In this controller, two experiments have been conducted with respect to the setpoint changes and load disturbance. The first experiment considers the change in setpoint temperature in steps of 10oC from 50oC to 70oC for three different rates of flow of water. In the second experiment the load disturbance in terms of addition of 100ml/min of water at three different time intervals is introduced in the system. It has been shown that, in these situations, the proposed controller adjusts NN weights which are equivalent to PID parameters in both the cases to achieve better control than conventional PID. In the proposed controller, an error less than 0.08oC have been achieved under the effect of the load disturbance. Moreover, it is also seen that the present controller gives error less than 0.11oC, 0.12oC and 0.12oC, without overshoot for 50oC, 60oC and 70oC, respectively, for all three rate of flow of water.
CITED BY (7)  
1 Roper, D. (2013). Energy based control system designs for underactuated robot fish propulsion.
2 LD, V. A. Simulation of Neuro-PID Controller for Pressure Process.
3 Thilagalakshmi, A. (2013, July). Simulation of Neuro-PID Controller for Pressure Process. In IJCA Proceedings on International Conference on Innovations In Intelligent Instrumentation, Optimization and Electrical Sciences (No. 9, pp. 18-21). Foundation of Computer Science (FCS).
4 Ivaniuk, D. Neuro-PID Controller for a Pasteurizer.
5 Verma, O. P., Singla, R., & Kumar, R. (2012). Intelligent Temperature Controller for Water Bath System. World Academy of Science, Engineering and Technology, International Journal of Computer, Information, Systems and Control Engineering, 6(9).
6 Prabhu, K., & Bhaskaran, V. M. (2012). Optimization of a control loop using adaptive method. Optimization, 1(3).
7 Tan, M. K., Chin, Y. K., Tham, H. J., & Teo, K. T. K. (2011, December). Genetic algorithm based PID optimization in batch process control. In Computer Applications and Industrial Electronics (ICCAIE), 2011 IEEE International Conference on (pp. 162-167). IEEE.
1 Google Scholar 
2 CiteSeerX 
3 refSeek 
4 iSEEK 
5 Scribd 
6 SlideShare 
7 PdfSR 
1 M. Khalid, S. Omatu and R. Yusof, “MIMO furnace control with neural networks,” IEEE Trans.Contr. Syst. Technol., vol. 1, pp. 238–245, 1993.
2 J. Tanomaru, S. Omatu, “Process Control by On-line Trained Neural Controllers,” IEEE Transactions on Industrial Electronics, vol. 39,pp. 511-521, 1992.
3 M. Khalid and S. Omatu, “A neural network controller for a temperature control system,”IEEE Contr. Syst., vol. 12, pp. 58–64, June 1992.
4 W. Wu, J. Yuan and L. Cheng, “Self-tuning sub-optimal control of time-invariant systems with bounded disturbance,” in Proc. of the 2005 American Control Conference., vol. 2,2005, pp. 876–882.
5 C. Y. Guo, Q. Song, and W. J. Cai, “Supply Air Temperature Control of AHU with a Cascade Control Strategy and a SPSA Based Neural Controller,” in Proceedings of the 2005 International Joint Conference on Neural Networks, vol. 4, 2005, pp. 2243-2248.
6 S. Omatu, T. Iwasa, M. Yoshioka, “Skill-based PID Control by Using Neural Networks,” in Proceedings of the 1998 IEEE International Conference on System Man and Cybernetics,vol. 2, 1998, pp. 1972-1977.
7 Q. H. Hu, A. T. P. So, W. L. Tes and A. Dong, “Use of Adaline PID Control for a Real MVAC System,” Proceedings of the 2005 International Conference on Wireless Communications,Networking and Mobile Computing, vol. 2, 2005, pp. 1374 – 1378.
8 K. J. Astrom and T. Hagglund, “Automatic Tuning of Simple Regulators with Specifications on Phase and Amplitude Margins,” Automatica, vol. 20, pp. 645-651, 1984.
9 C. C. Hang, K.J. Astrom and W.K. Ho, “Refinements of the Ziegle-Nichols tuning formula,”in 1991 IEE proceedings Pt. D, Control theory & Applications, vol.138, no. 2, 1991, pp. 111-118.
10 W. K. Ho, C. C. Hang and L. S. Cao, “Tuning of PID Controllers Based on Gain and Phase Margin Specifications, Automatica, vol.31, no. 3, pp. 497-502, 1995.
11 K. J. Astrom, C. C. Hang, P. Persson and W. K. Ho, “Toward Intelligent PID Control,”Automatica, vol.28., no. 1, pp.1-9, 1992.
12 W. K. Ho, O. P. Gan, E. B. Tay and E. L. Ang, “Performance and Gain and Phase Margins of Well Known PID Tuning Formulas,” IEEE Trans. On Control Systems Technology, vol.4,pp.473-477, 1996.
13 W. K. Ho, C. C. Hang and J. H. Zhou, “Performance and Gain and Phase Margins of WellKnown PI Tuning Formula,” IEEE Trans. On Control Systems Technology, vol.3, no. 2,pp.245-248, 1995.
14 F. Cameron and D.E. Seborg, “A self-tuning controller with a PID structure,” Int. J. Control vol. 30, pp. 401-417, 1983.
15 D.W. Clark and P.J. Gawthrop, “Self-tuning control,” in Proc. IEE, Pt-D, vol. 126, 1979, pp.633-640.
16 R. Ortega and R. Kelly, “PID self-tuners: Some theoretical and practice aspects,” IEEE Trans. Ind. Electron, vol. 31, pp. 312, 1984.
17 C.G. Proudfoot, P.J. Gawthrop and O.L.R. Jacobs, “Self-tuning PI control of a pH neutralization process,” in Proc. IEE, Pt-D, vol. 130, 1983, pp. 267-272.
18 F. Radke and R. Isermann, “A parameter-adaptive PID controller with stepwise parameter optimization,” Automatic, vol. 23, pp. 449-457, 1987.
19 B. Wittenmark, “Self-tuning PID Controllers Based on Pole Placement,” Lund Institute Technical Report, TFRT-7179, 1979.
20 D. E. Rumelhart and J. L. McClelland, “Parallel Distributed Processing,” vol. 1, MIT Press,Cambridge, MA, 1986.
21 J. H. Taylor and K. J. Astrom, “A non-linear PID auto tuning algorithm”, American Automatic control conference, Seattle, W.A., 1986, pp. 1-6.
22 M. A. Unar, D. J. Murray-Smith and S. F. Ali Shah, “Design and tuning for fixed structure PID controllers—A survey”, report CSC-96016, Centre for systems and control & department of mechanical Engineering, university of Glaslow, 1996.
23 A. E. B. Ruano, P. J. Fleming and D. I. Jones, “Connectionist approach to PID autotuning,”in IEE proceedings-D, vol. 139 (3), 1992, pp. 279-285.
24 K. C. Chan, S. S. Leong and G. C. I. Lin, “A neural network PI controller tuner,” Artificial Intelligence in Engineering, vol. 9, pp. 167-176, 1995.
25 C. L. Chen and F. Y. Chang, “Design and analysis of neural/fuzzy variable structural PID control systems,” in IEE Proceedings Control Theory Application, vol. 143 (2), 1996, pp.200-208.
26 V. VanDoren, “Model free adaptive control”, Control engineering, Europe, pp. 25-31, 2001.
27 M. Khalid, S. Omatu, “A neural network controller for a temperature control system,” IEEE Contr. Syst. Mag., vol. 12, pp. 58-64, 1990.
28 A. G. Barto, “Connectionist learning for control,” in W. T. Miller, 111, R. S. Sutton, P. J.Werbos, eds., Neural Networks for Control. Cambridge, MA: MI, 1990.
29 B. Widrow, S. D. Steams, “Adaptive Signal Processing,” Englewood Cliffs, NJ: Prentice Hall,1985.
30 D. Psaltis, A. Sideris, A. Yamamura, “A multilayered neural network controller,” IEEE Control Syst. Mag., vol. 10, pp. 44-48, 1988.
31 K. S. Narendra, K. Parthasarathy, “Identification and control of dynamical systems using neural networks,” IEEE Trans.Neura1 Networks, vol. 1, pp. 4-27, 1990.
32 P. J. Werbos, “Backpropagation through time: What it does and how to do it?,” in Proc.IEEE. 78, 1990, pp. 1550-1560.
33 D. H. Nguyen and B. Widrow, “Neural networks for self-leaming control systems,” IEEE Control Syst. Mag., vol. 10, pp. 18-23, 1990.
34 M. Jordan and D. E. Rumelhart, “Forward models: Supervised learning with a distal teacher,”Cognitive Science., vol. 16.pp. 307-354, 1992.
35 A. N. Ponce, A. A. Behar, A. O. Hernandez and V. R. Sitar, “Neural Network for Self-tuning Control Systems”, Acta Polytechnica, vol. 44, pp.49-52, 2004.
Mr. Nisha Jha
University of Delhi South Campus - India
Dr. Udaibir Singh
Acharya Narendra Dev College University of Delhi - India
Dr. T.K. Saxena
National Physical Laboratory - India
Professor Avinashi Kapoor
University of Delhi South Campus - India