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Title: A study of linearization and decoupling techniques applied to mimo nonlinear interacting processes
Researcher: Subbulekshmi D
Guide(s): Kanakaraj J
Keywords: Electrical engineering
linearization and decoupling techniques
Multi Input Multi Output
Upload Date: 21-Feb-2014
University: Anna University
Completed Date: 01/11/2013
Abstract: Typical industrial chemical plants are tightly integrated processes newlinewhich exhibit nonlinear behavior and complex dynamic properties. They newlineusually have two or more controlled variables requiring two or more newlinemanipulated variables. Those processes with more than one controlled newlinevariable and more than one manipulated variable are known as Multi Input newlineMulti Output (MIMO) process. The MIMO system can be decoupled into newlineSISO systems. If a MIMO system is considered as a decoupled SISO system, newlineno interaction will exist among the SISO subsystems. Each input variable has newlineto control only one output variable. The essence of decoupling is to cancel the newlineinteraction existing process, allowing independent control of the loops. A newlinemultivariable system experiences interactions and responds poorly. The newlineobjective in decoupling is to compensate the effect of interactions brought newlineabout by cross coupling of the process variables and cause the input output newlinerelationship to be linear. For decoupling a linear interacting system, RGA and newlineRNGA methods are applied. Implementation of these algorithm results in the newlinesystem to be decoupled. Compared to RGA, RNGA method shows better results. But for a nonlinear highly interacting system, this method does not newlineprovide satisfactory results. This algorithm works well for linear systems newlineonly. So the algorithms like Kravaris, Generic model control, and Hirschorn s newlinealgorithm are considered. The objectives of this study are to analyze the newlinenature of interaction and understand the concepts of three Decoupling and Linearization algorithms namely (i) Kravaris algorithm (ii) Generic Model newlineControl algorithm and (iii) Hirschorn s algorithm. PI, PI-SPW and FLC newlinecontrollers are also included along with the linearization algorithms to newlineenhance the performance of the system. MPC controller is also with newlineHirschorn s algorithm to achieve best results. Optimization algorithm like newlineGenetic Algorithm is implemented to obtain a suitable controller.
Pagination: xx, 202p.
Appears in Departments:Faculty of Electrical and Electronics Engineering

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02_certificate.pdf225.04 kBAdobe PDFView/Open
03_abstract.pdf12.09 kBAdobe PDFView/Open
04_acknowledgement.pdf6 kBAdobe PDFView/Open
05_contents.pdf29.4 kBAdobe PDFView/Open
06_chapter 1.pdf110.64 kBAdobe PDFView/Open
07_chapter 2.pdf101.01 kBAdobe PDFView/Open
08_chapter 3.pdf524.87 kBAdobe PDFView/Open
09_chapter 4.pdf750.78 kBAdobe PDFView/Open
10_chapter 5.pdf574.09 kBAdobe PDFView/Open
11_chapter 6.pdf893.5 kBAdobe PDFView/Open
12_chapter 7.pdf825.69 kBAdobe PDFView/Open
13_chapter 8.pdf16 kBAdobe PDFView/Open
14_references.pdf24.14 kBAdobe PDFView/Open
15_publications.pdf8.9 kBAdobe PDFView/Open
16_vitae.pdf5.71 kBAdobe PDFView/Open

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