Support Vector Regression Based Nonlinear Model Reference Adaptive Control
- 1 December 2012
- journal article
- Published by Trans Tech Publications, Ltd. in Advanced Materials Research
- Vol. 601, 289-293
- https://doi.org/10.4028/www.scientific.net/amr.601.289
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.This publication has 14 references indexed in Scilit:
- Adaptive support vector regression for UAV flight controlNeural Networks, 2011
- Support vector regression with reduced training sets for air temperature prediction: a comparison with artificial neural networksNeural Computing & Applications, 2010
- Comparing Support Vector Machines and Feedforward Neural Networks With Similar Hidden-Layer WeightsIEEE Transactions on Neural Networks, 2007
- Online SVM regression algorithm-based adaptive inverse controlNeurocomputing, 2007
- MRAC Combined Neural Networks for Ultra-Sonic MotorJSME International Journal Series C, 2006
- Adaptive control of a class of nonlinear discrete-time systems using support vector machinePublished by Institute of Electrical and Electronics Engineers (IEEE) ,2004
- A tutorial on support vector regressionStatistics and Computing, 2004
- Neural-network-based model reference adaptive systems for high-performance motor drives and motion controlsIEEE Transactions on Industry Applications, 2002
- The Nature of Statistical Learning TheoryPublished by Springer Science and Business Media LLC ,1995
- Comparative studies of model reference adaptive control systemsIEEE Transactions on Automatic Control, 1973