Please use this identifier to cite or link to this item: http://scholarbank.nus.edu.sg/handle/10635/15781
Title: Data-based methods for modeling, control and monitoring of chemical processes
Authors: CHENG CHENG
Keywords: Nonlinear Process, JITL, Process Modeling, Controller design, Process Monitoring
Issue Date: 21-Feb-2007
Source: CHENG CHENG (2007-02-21). Data-based methods for modeling, control and monitoring of chemical processes. ScholarBank@NUS Repository.
Abstract: A new JITL methodology is proposed by using both distance measure and angle measure to evaluate the similarity between data samples to improve the modeling accuracy. Based on the enhanced JITL technique, a composite model consisting of a nominal ARX model and JITL is employed to model the process behaviour in the operating space. The state space realizations of the resulting model and PID controller are then reformulated as an uncertain system, by which the robust stability analysis by using the structured singular value test can be developed as the design criterion of robust PID controller design. Next, by incorporating JITL into the controller design, three data-based adaptive control strategies are proposed: adaptive single-neuron (ASN) controller, adaptive IMC controller and auto-tuning PID controller for nonlinear process control. Last, by integrating JITL and principal component analysis (PCA) into a JITL-PCA scheme, a new monitoring method is proposed for dynamic nonlinear process.
URI: http://scholarbank.nus.edu.sg/handle/10635/15781
Appears in Collections:Ph.D Theses (Open)

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