Effect of Adaptation Gain in Model Reference Adaptive Controlled Second Order System

P. Swarnkar, S. Jain, R. K. Nema

Abstract


Adaptive control involves modifying the control law used by the controller to cope with the fact that the parameters of the system being controlled change drastically due to change in environmental conditions or in system itself. This technique is based on the fundamental characteristic of adaptation of living organism. The adaptive control process is one that continuously and automatically measures the dynamic behavior of plant, compares it with the desired output and uses the difference to vary adjustable system parameters or to generate an actuating signal in such a way so that optimal performance can be maintained regardless of system changes. Nature of adaptation mechanism for controlling the system performance is greatly affected by the value of adaptation gain. It is observed that for the lower order system wide range of adaptation gain can be used to study the performance of the system. As the order of the system increases the applicable range of adaptation gain becomes narrow. This paper deals with application of model reference adaptive control scheme to second order system with different values of adaptation gain. The rule which is used for this application is MIT rule. Simulation is done in MATLAB and simulink and the results are compared for varying adaptation mechanism due to variation in adaptation gain.


Keywords


model reference; adaptive control; MIT rule; adaptation; mechanism; gain

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References


D. Simon, “Kalman filtering with state constraints- a survey of linear and nonlinear algorithm”, Control Theory and Application, IET, Vol. 4, No. 8, pp. 1303–1318, 2010

Rey-Chue Hwang, Huang-Chu Huang, Wei-Shen Chi, “A New Fuzzy PID-Like Controller”, IEEE International Conference on Systems, Man and Cybernetics, Nashville, Vol. 5, 2000

K. S. Tang, Kim Fung Man, Guanrong Chen, Sam Kwong, “An Optimal Fuzzy PID Controller”, IEEE Transactions on Industrial Electronics, Vol. 48, No. 4, pp. 757-765, 2001

K. L. Lo, M. O. Sadegh, “Systematic Method for the Design of A Full-Scale Fuzzy PID Stability Controller for SVC to Control Power System”, IEEE Transaction on Generation Transmission and Distribution Vol. 150, No. 3, pp. 297–304, 2003

E. M. Jafarov, M. N. A. Parlakçı, Y. Istefanopulos, “A New Variable Structure PID-Controller Design for Robot Manipulators”, IEEE Transactions on Control Systems Technology, Vol. 13, No. 1, pp. 122-130, 2005

Tae-Yong Choi, Kap-Ho Seo, Jin-Ho Shinü, Ju-Jang Lee, “The Hybrid SOF-PID Controller for A MIMO Nonlinear System”, Proceedings of the 2005 IEEE/ASME, International Conference on Advanced Intelligent Mechatronics Monterey, California, USA, pp. 825-830, 2005

B. Jia, G. Ren, G. Long, “Design and Stability Analysis of Fuzzy Switching PID Controller”, 6th World Congress on Intelligent Control and Automation, Dalian, China, pp. 3934-3938, 2006

A. Rubaai, M. J. Castro-Sitiriche, A. Ofoli, “DSP-Based Implementation of Fuzzy-PID Controller Using Genetic Optimization for High Performance Motor Drives”, IEEE International Conference on Industry Applications, New Orleans, LA, pp. 1649–1656, 2007

A. Rubaai, M. J. Castro-Sitiriche, A. Ofoli, “DSP-Based Laboratory Implementation of Hybrid Fuzzy-PID Controller Using Genetic Optimization for High-Performance Motor Drives”, IEEE Transactions on Industry Applications, Vol. 44, No. 6, pp. 1977–1986, 2008

D. Sun, J. Meng, “A Single Neuron PID Controller Based PMSM DTC Drive System Fed by Fault Tolerant 4-Switch 3-Phase Inverter”, IEEE International Conference on Industrial Electronics and Applications, Singapore, pp. 1-5, 2006

J. Yao, L. Wang, C. Wang, Z. Zhang, P. Jia, “ANN-Based PID Controller for An Electro-Hydraulic Servo System”, IEEE International Conference on Automation and Logistics, Qingdao, China, pp. 18–22, 2008

Xue-Kui Wang, Xu-Hong Yang, Gang Liu, Hong Qian, “Adaptive Neuro-Fuzzy Inference System PID Controller for SG Water Level of Nuclear Power Plant”, Proceedings of The Eighth International Conference on Machine Learning and Cybernetics, Baoding, pp. 567–572, 2009

K. Benjelloun, H. Mechlih, E. K. Boukas, “A Modified Model Reference Adaptive Control Algorithm for DC Servomotor”, Second IEEE Conference on Control Applications, Vancouver, B. C., Vol. 2, pp. 941-946, 1993

M. S. Ehsani, “Adaptive Control of Servo Motor by MRAC Method”, IEEE International Conference on Vehicle, Power and Propulsion, Arlington, TX, pp. 78–83, 2007

M. Kirar, P. Swarnkar, S. Jain, R. K. Nema, “Comparative Study of Conventional and Adaptive Schemes for DC Servomotors”, International Conference on Energy Engineering ICEE, Puducherry, India, 2009

P. Swarnkar, S. Jain, R. K. Nema, “Application of Model Reference Adaptive Control Scheme To Second Order System Using MIT Rule”, International Conference on Electrical Power and Energy Systems (ICEPES-2010), MANIT, Bhopal, India, 2010

P. Swarnkar, S. Jain, R. K. Nema, “Effect of Adaptation Gain on System Performance for Model Reference Adaptive Control Scheme Using MIT Rule”, International Conference of World Academy of Science, Engineering and Technology, Paris, FRANCE, 2010

Pin-Yan Tsai, Hung Chu Huang, Yu-Ju-Chen, Rey-Chue Hwang, “The Model Reference Control by Auto Tuning PID-Like Fuzzy Controller”, International Conference on Control Applications, Taipei, Taiwan, 2004

M. Cirrincione, M. Pucci, “An MRAS-Based Sensorless High Performance Induction Motor Drive With A Predictive Adaptive Model”, IEEE Transaction on Industrial Electronics, Vol. 52, No. 2, pp. 532, 2005

Kuo-Ming Chang, “Model Reference Adaptive Control for Uncertain Systems With Sector-Like Bounded Nonlinear Inputs”, International Conference on Control and Automation, Budapest, Hungary, 2005

M. T. Benchouia, A. Ghamri, M. E. H. Benbouzid, A. Golea, S. E. Zouzou, “Fuzzy Model Reference Adaptive Control of Power Converter for Unity Power Factor and Harmonics Minimization”, International Conference on Electrical Machine and System, Seoul, Korea, 2007

T. John Koo, “Stable Model Reference Adaptive Fuzzy Control of A Class of Nonlinear Systems”, IEEE Transactions on Fuzzy Systems, Vol. 9, No. 4, pp. 624–636, 2001

Yuan-Rui Chen Jie Wu, Norbert C. Cheung, “Lyapunov’s Stability Theory-Based Model Reference Adaptive Control for Permanent Magnet Linear Motor Drives”, 1st IEEE International Conference on Power Electronics System and Applications, Hong Kong, China, pp. 260 - 266, 2004

Kuo-Kai Shyu, Ming-Ji Yang, Yen-Mo Chen, Yi-Fei Lin, “Model Reference Adaptive Control Design for A Shunt Active-Power-Filter System”, IEEE Transactions on Industrial Electronics, Vol. 55, No. 1, pp. 97–106, 2008

K. J. Astrom, Bjorn Wittenmark, “Adaptive Control”, 2nd Ed. Pearson Education Asia, pp. 185-225, 2001

Pankaj Swarnkar, “Automatic Control System”, 3rd Ed. Satya Prakashan, New Delhi, 2010




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