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首頁> 中文期刊> 《東華大學(xué)學(xué)報：英文版》 >Eigen-Space Decomposition(ESD)Method for the Design of Internal Model Controller(IMC)from Noisy Input and Output Plant Data

Eigen-Space Decomposition(ESD)Method for the Design of Internal Model Controller(IMC)from Noisy Input and Output Plant Data

     
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摘要

A novel approach to design Internal Model Controller(IMC)is proposed in this paper directly from measuredinput and output plant data,which are assumed to becontaminated by measurement noise.In order to avoidthe complicated structure-identification problem inmost cases,two Finite Impulse Response(FIR)modelsare taken to represent the plant model and the internalmodel controller respectively.Taking account of mea-surement noise both in the plant input and its output,anESD based Total Least Squares(TLS)solution is appliedfor the unbiased identification of the plant model and itsinverse model,the latter constitutes the internal modelcontroller according to the principle that the internalmodel controller approximates the inverse dynamics ofthe plant model.Simulations are given for a testifica-tion.

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