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A new class of dual support vector machine NPID controller used for predictive control
Author(s) -
Wang Decheng,
Lin Hui
Publication year - 2015
Publication title -
ieej transactions on electrical and electronic engineering
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.254
H-Index - 30
eISSN - 1931-4981
pISSN - 1931-4973
DOI - 10.1002/tee.22105
Subject(s) - support vector machine , control theory (sociology) , pid controller , dual (grammatical number) , nonlinear system , controller (irrigation) , computer science , model predictive control , compensation (psychology) , transfer function , artificial intelligence , control engineering , engineering , control (management) , temperature control , art , psychology , agronomy , physics , literature , electrical engineering , quantum mechanics , psychoanalysis , biology
A nonlinear proportional‐integral‐differential (NPID) controller used for predictive control tunes the proportional, integral, and differential gain coefficients according to the system prediction output. The prediction error has great influence on its performance. In this paper, we propose a dual support vector machine (SVM) NPID controller using an SVM prediction system output with less error. System model reflecting the system feature is constructed by SVM, based on the training dataset gained by system transfer function. The error compensation model is also constructed by SVM, based on prediction error with the above system model. The system prediction output is obtained by the aforementioned SVM models. And it is used to compute the PID gain coefficients. Simulation results of typical systems show that the proposed method has very little prediction error and high control performance. © 2015 Institute of Electrical Engineers of Japan. Published by John Wiley & Sons, Inc.