Closed-loop identification and predictive control of chemical processes /
Model Predictive Control (MPC) is currently the most
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| Format: | Thesis Book |
| Language: | English |
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[Place of publication not identified] :
[publisher not identified] ;
1996.
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| Subjects: | |
| Online Access: | http://proxy.library.tamu.edu/login?url=http://proquest.umi.com/pqdweb?did=739667721&sid=1&Fmt=2&clientId=2945&RQT=309&VName=PQD |
| Summary: | Model Predictive Control (MPC) is currently the most effective control scheme in the process industry. The popularity of MPC lies in the fact that it can handle process input/output constraints systematically during the design and the implementation of the controller. MPC relies on a process model to generate optimal inputs by performing an on- line optimization. The process model is usually made available at the controller design stage by performing open- loop experiments on the process. The closed-loop performance depends on the accuracy of the model and may become unacceptable as the modeling error increases. Hence, due to safety and performance reasons, it may become necessary to update the process model under closed-loop conditions. In this work, a new approach to simultaneous constrained Model Predictive Control and Identification (WPCI) is developed. In this approach, attempts are made to update transfer function process models with minimum deterioration in the control performance while satisfying the process input/output constraints. The method relies on the persistence of excitation condition, which is used to produce dynamic information about the process under feedback control. The persistence of excitation condition, a function of the process inputs and the process outputs, is first converted into a condition involving only the process inputs. The resulting new condition on the inputs is used as an additional constraint in the conventional MPC formulation. The resulting non-convex on-line optimization problem is transformed into a convex problem; the solution of which is obtained by solving a series of converging semidefinite programming problems. A deterministic global optimization technique, based on the branch-and-bound approach is also developed to obtain the global solution of the on-line optimization problem The global solution can be used to measure the performance of the semidefinite programming approach. The applicability of MPCI to update dynamic process models is demonstrated through various simulations. The results obtained show that MPCI gives superior performance as compared to the traditional methods in terms of (a) handling process input/output constraints, and (b) keeping the deterioration of the output regulation to a minimum value, while performing closed-loop identification. |
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| Item Description: | Vita. "Major Subject: Chemical Engineering". |
| Physical Description: | xiii, 162 leaves : illustrations ; 28 cm. Issued also on microfiche from University Microfilms Inc. |
| Bibliography: | Includes bibliographical references: pages 133-138. |