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PERFORMANCE MONITORING AND DISTURBANCE
ADAPTATION FOR MODEL PREDICTIVE CONTROL
by
Zhijie Sun
A Dissertation Presented to the
FACULTY OF THE USC GRADUATE SCHOOL
UNIVERSITY OF SOUTHERN CALIFORNIA
In Partial Fulfillment of the
Requirements for the Degree
DOCTOR OF PHILOSOPHY
(CHEMICAL ENGINEERING)
December 2012
Copyright 2012 Zhijie Sun
Object Description
| Title | Performance monitoring and disturbance adaptation for model predictive control |
| Author | Sun, Zhijie |
| Author email | zhijiesu@usc.edu;sunzhijie@gmail.com |
| Degree | Doctor of Philosophy |
| Document type | Dissertation |
| Degree program | Chemical Engineering |
| School | Viterbi School of Engineering |
| Date defended/completed | 2012-08-15 |
| Date submitted | 2012-08-23 |
| Date approved | 2012-08-27 |
| Restricted until | 2012-08-27 |
| Date published | 2012-08-27 |
| Advisor (committee chair) | Qin, S. Joe |
| Advisor (committee member) |
Wang, Pin Safonov, Michael G. |
| Abstract | Model predictive control (MPC) is a widely used advanced process control technique in the process industry. According to the internal model principle, the internal model of MPC has to include both exact plant and disturbance models to be optimal. However, in practice, the MPC usually assumes a step-like disturbance or a fixed disturbance model. As a result, the MPC will be suboptimal when disturbance changes slowly. Moreover, it lacks a tool assessing the optimality of control performance in terms of the MPC model. ❧ In this dissertation, a new MPC disturbance adaptation method is presented. Starting from a single-input-single-output (SISO) semiconductor manufacturing process, we replaced the conventional run-to-run controller by an adaptive EWMA controller. It is shown that the plant model mismatch can be compensated by adapting the disturbance model. Analysis has been done to show that the adaptive controller is stable and converges to the optimal controller. ❧ The proposed method is then extended to multi-input-multi-output (MIMO) systems. For the ease of practical applications, the integrated moving average (IMA) model with order (1,1) is recommended. The equivalence between the IMA(1,1) parameter and the prediction error filter constant in commercial MPC has been established. Implementation of disturbance adaptation is explained. ❧ Another disturbance modeling tool is presented. It focuses on the closed-loop identification of a nonparametric disturbance model. The method incorporates the plant model information during the conversion from observer Markov parameters to system Markov parameters. ❧ A new control performance assessment method evaluating MPC model quality is then presented. Feedback invariant principle is introduced, based on which a method estimating disturbance innovations is given. A model quality index is developed as the performance benchmark, which compares prediction errors with disturbance innovations. It is shown that the model quality index related to the MPC performance index. ❧ Most industrial processes are optimized by a linear programming (LP) problem on top of the MPC. A new control performance monitoring method for cascaded LP-MPC system is developed. The block-lower-triangular interactor matrix is introduced to form a new method that is able to determine the performance benchmark based on controlled variable (CV) priorities coming from the LP results. |
| Keyword | model predictive control; run-to-run control; control performance monitoring |
| Language | English |
| Part of collection | University of Southern California dissertations and theses |
| Publisher (of the original version) | University of Southern California |
| Place of publication (of the original version) | Los Angeles, California |
| Publisher (of the digital version) | University of Southern California. Libraries |
| Provenance | Electronically uploaded by the author |
| Type | texts |
| Legacy record ID | usctheses-m |
| Rights | Sun, Zhijie |
| Access conditions | The author retains rights to his/her dissertation, thesis or other graduate work according to U.S. copyright law. Electronic access is being provided by the USC Libraries in agreement with the author, as the original true and official version of the work, but does not grant the reader permission to use the work if the desired use is covered by copyright. It is the author, as rights holder, who must provide use permission if such use is covered by copyright. The original signature page accompanying the original submission of the work to the USC Libraries is retained by the USC Libraries and a copy of it may be obtained by authorized requesters contacting the repository e-mail address given. |
| Repository name | University of Southern California Digital Library |
| Repository address | USC Digital Library, University of Southern California, University Park Campus MC 7002, 106 University Village, Los Angeles, California 90089-7002, USA |
| Repository email | cisadmin@usc.edu |
| Archival file | uscthesesreloadpub_Volume4/etd-SunZhijie-1167.pdf |
Description
| Title | Page 1 |
| Full text | PERFORMANCE MONITORING AND DISTURBANCE ADAPTATION FOR MODEL PREDICTIVE CONTROL by Zhijie Sun A Dissertation Presented to the FACULTY OF THE USC GRADUATE SCHOOL UNIVERSITY OF SOUTHERN CALIFORNIA In Partial Fulfillment of the Requirements for the Degree DOCTOR OF PHILOSOPHY (CHEMICAL ENGINEERING) December 2012 Copyright 2012 Zhijie Sun |
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