Support Vector Regression Based Nonlinear Model Reference Adaptive Control

Abstract:

Model reference adaptive control (MRAC) is widely used in linear system control areas, and Neural Networks (NN) is often used to extend MRAC to nonlinear areas. However, this kind of solution inherits some drawbacks of NN, including slow learning speed, weak generalization ability, local minima tendency, etc. Given these drawbacks, this paper attempts to use support vector regression (SVR) as a substitute of NN. In this approach, SVR is employed to compensate the nonlinear part of the plant. A stable controller-parameter adjustment mechanism is constructed by using the practical stability theory. Simulation results show that the proposed approach could reach desired performance.

• Periodical: Advanced Materials Research (Volume 601)
• Pages: 289-293
• DOI: https://doi.org/10.4028/www.scientific.net/AMR.601.289
• Online since: December 2012
• Authors: Xin Xin Ye, Jian Gang Lu, Qin Min Yang
• Keywords: Model Reference Adaptive Control, Practical Stability, Support Vector

References

[1] Hang C C, Parks P C. Comparative Studies of Mode Reference Adaptive Control System[J]. IEEE Trans, Automatic Control, 1973, 18 (5): 419-428.

DOI: 10.1109/tac.1973.1100361

[2] Lu J, Phuah J, Yahagi T. A Method of Mode Reference Adaptive Control for MIMO Nonlinear System Using Neural Networks[J]. IEICE Trans, Fundamentals, 2001, 84(8): 1933-(1941).

[3] Rossomando F G, Soria C, Patino D, Carelli R. Model Reference Adaptive Control for Mobile Robots in Trajectory Tracking using Radial Basis Function Neural Networks[J]. Latin American Applied Research, 2011, 41(8 ): 177-182.

[4] Elbuluk M E, Tong L, Husain I. Neural Network based Model Reference Adaptive Systems for High Performance Motor Drives and Motion Controls[J]. IEEE Trans, Industry Applications, 2000, 38(3): 879-886.

DOI: 10.1109/tia.2002.1003444

[5] Tanaka Kanya, Yoshimura Yoshie. MRAC Combined Neural Networks for Ultra-Sonic Motor[J]. JSME International Journal Series-C, 2006, 49(4): 1084-1090.

DOI: 10.1299/jsmec.49.1084

[6] Wang Hui, Pi Daoying, Sun Youxian. Online SVM Regression Algorithm-based Adaptive Inverse Control[J]. Neurocomputing, 2006, 70(4): 952-959.

DOI: 10.1016/j.neucom.2006.10.021

[7] Vapnik V N. The Nature of Statistical Learning Theory, Springer[M]. New York, USA, (1995).

[8] Smola A J, Scholkopf B. A tutorial on support vector regression[J]. Statistics and Computing, 2004, 14(3): 199-222.

DOI: 10.1023/b:stco.0000035301.49549.88

[9] Romero Enrique, Toppo Daniel. Comparing Support Vector Machines and Feedforward Neural Networks With Similar Hidden-Layer Weights[J]. IEEE Transaction on Neural Network, 2007, 18(3): 959-963.

DOI: 10.1109/tnn.2007.891656

[10] Chevalier Robert F, Hoogenboom Gerrit, McClendon Ronald W. Support vector regression with reduced training sets for air temperature prediction a comparison with artificial neural networks[J]. Neural Computing & Applicatio, 2011, 20(1): 151-159.

DOI: 10.1007/s00521-010-0363-y

[11] Xu J, Chen S. Adaptive Control of A Class of Nonlinear Discrete-time Systems Using Support Vector Machine[C]. Proceedings of the Fifth World Congress on Intelligent Control and Automation. Hangzhou, 2004: 440-443.

DOI: 10.1109/wcica.2004.1340610

[12] Shin Jongho, Kim H Jin, Kim Youdan. Adaptive Support Vector Regression for UAV Flight Control[J]. Neural Networks , 2011, 24(1): 109-120.

DOI: 10.1016/j.neunet.2010.09.011

[13] Cheng Xi Xue, Poo Aun-Neow, Chou Slaw Kiang. Support Vector Regression Model Predictive Control on a HVAC Plant[J]. Control Engineering Practice, 2007, 15(8): 897-908.

DOI: 10.1016/j.conengprac.2006.10.010

[14] Shin Jongho, Kim H Jin, Park Sewook. Model Predictive Flight Control Using Adaptive Support Vector Regression[J]. Neurocomputing, 2010, 73(4): 1031-1037.

DOI: 10.1016/j.neucom.2009.10.002

[15] Cherkassky V, Ma Y. Practical Selection of SVM Parameters and Noise Estimation for SVM Regression[J]. Neural Networks, 2004, 17: 113-126.

DOI: 10.1016/s0893-6080(03)00169-2

[16] Parrella F. Online Support Vector Regression[D]. Genoa: University of Genoa, (2007).

[17] Fossen T I. High performance ship autopilot with wave filter[C]. Proc. Ship Control System Symp., Ottawa, 1993: 25-29.

[18] J Van Amerongen. Adaptive Steering of Ships—A model Reference Approach[J]. Automatica, 1984, 20: 3-14.

DOI: 10.1016/0005-1098(84)90060-8