System identification and model-based control for a class of distributed parameter systems

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Title: System identification and model-based control for a class of distributed parameter systems
Author: Zheng, Daguang
Abstract: A large number of processes in the chemical and petroleum industries are distributed in nature . The diversity of distribution patterns and functions make modeling of distributed parameter systems (DPS ) a very challenging problem . Employment of first principles about the physics and chemistry will yield mathematical models in the form of systems of linear or nonlinear partial differential equations (PDEs ) . The analytical and numerical solutions for PDEs are infinite or very high dimension , which are not suitable for implementable control designs . The first objective of this research is to develop a general model reduction methodology to reduce the system of PDEs to a finite dimensional system of ODEs , which can be used to synthesize a model -based control . This methodology is based on the identification of empirical eigenfunctions (EEFs ) from data and using the Galerkin method to obtain a model with dominant modes . For a system of first -order hyperbolic PDEs , accelerated EEFs are used to find a reduced -order model . In the case where the physics -based modeling approach cannot be applied with confidence , an input /output model developed based on experimental data may suffice . A novel system identification method to develop the model using a data -driven approach is proposed . The method combines the fundamental principles of singular value decomposition (SVD ) and Karhunen -Loeve (KL ) expansion in the identification of a finite order model . The application of SVD and KL provides natural decoupling of the inputs and outputs while yielding a model that captures the dominant spatial and temporal behavior of the distributed system . The fundamental theorems to assure the accuracy of this method are provided . Implementable control designs to regulate the DPS are now realizable with a finite order model . Dynamic matrix control (DMC ) and Quadratic DMC (QDMC ) are selected as control strategies , wherein the merit of the control design is dependent on the fidelity of the identified by SVD -KL method . Sufficient conditions are proposed to tune the QDMC control strategy so that stable closed -loop performance is guaranteed . The regulation of several candidate chemical reactor systems and the hydro -dealkylation process that produces benzene from toluene (HDA ) are used to illustrate the potential of this data -driven modeling and model -based control framework for distributed parameter systems .
URI: http : / /hdl .handle .net /2346 /13527
Date: 2003-05


System identification and model-based control for a class of distributed parameter systems. Doctoral dissertation, Texas Tech University. Available electronically from http : / /hdl .handle .net /2346 /13527 .

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