Nonlinear Closed-Loop System Identification in The presence of Non-stationary Noise Source
Abstract
In this dissertation, nonlinear identi fication approaches are presented that construct Wienerand Hammerstein models. These are block-oriented models consisting of a memoryless nonlinearity either preceded or followed by a linear filter, respectively. The algorithms were developed to handle several practical challenges common in chemical process control applications. These challenges include systems running in closed-loop, incorporating non-stationary process disturbances, and with possibly unstable plant dynamics. Identifi cation methods based on the prediction error method are developed to address these challenges. One of the main factors required for successful application of PEM algorithms is having a good initial estimate of the system under study. In this work, Instrumental Variable scheme is used to initialize the Hammerstein models, and a non-iterative overparameterized algorithm is developed to initialize the Wiener models. In all cases, the algorithms are developed theoretically, and then validated using Monte Carlo simulations. The closed-loop Hammerstein identifi cation algorithms are validated using data from differential equation based simulation of a continuous stirred tank reactor.
Description
Keywords
Engineering--Electronics and Electrical
Citation
Aljamaan, I. (2016). Nonlinear Closed-Loop System Identification in The presence of Non-stationary Noise Source (Doctoral thesis, University of Calgary, Calgary, Canada). Retrieved from https://prism.ucalgary.ca. doi:10.11575/PRISM/27117