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Predictive control has attracted much attention from both industry and academia alike due to its intuitive time domain formulation and since it easily affords adaption. The time domain formulation enables the user to build in prior knowledge of the operating constraints and thus the process can be controlled more efficiently, and the adaptive mechanism provides tighter control for systems whose behaviour changes with time. This thesis presents a fusion of technologies for dealing with the more practical aspects of obtaining suitable models for predictive control, especially in the adaptive sense. An accurate model of the process to be controlled is vital to the success of a predictive control scheme, and most the of work to date has assumed that this model is of fixed order, a restriction which can lead to poor controller performance associated with under/overparameterisation of the estimated model. To overcome this restriction a strategy which estimates both the parameters and the order of a linear model of the time-varying plant online is suggested. This Multiple Model Least-Squares technique is based on the recent work of Niu and co-workers who have ingeniously extended Bierman's method of UD updating so that, with only a small change to the existing UD update code, a wealth of additional information can be obtained directly from the U and D matrices including estimates of all the lower order models and their loss functions. The algorithm is derived using Clarke's Lagrange multiplier approach leading to a neater derivation and possibly a more direct understanding of Niu's Augmented UD Identification algorithm. An efficient and robust forgetting mechanism is then developed by analysing the properties of the continuous-time differential equations corresponding to existing parameter tracking methods. The resulting Multiple Model Recursive Least-Squares estimator is also ported to the δ-domain in order to obtain models for predictive controllers that employ fast sampling. The MMRLS estimator is then used in an adaptive multiple model based predictive controller for a coupled tanks system to compare performance with the fixed model order case.

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This multi-phase project was designed (1) to evaluate existing post-harvest process controls and intervention strategies used to reduce Escherichia coli O157:H7, (2) to evaluate the impacts of cattle source and environmental factors on Salmonella prevalence in bovine lymph nodes, and (3) to evaluate sanitary conditions of feedyards in South Texas. The ultimate goal of this project was to identify and implement measures that reduce E. coli O157:H7 in beef harvest facilities, and Salmonella prevalence in feedyards. To evaluate process control of E. coli O157:H7 throughout the beef harvest process, samples were collected from harvest floor processing areas at two commercial beef slaughter establishments, and enumerated for aerobic plate counts, E. coli/coliform, and Enterobacteriaceae. To survey existing Salmonella prevalence, bovine lymph nodes (n = 307) were collected from beef carcasses at a commercial beef processing plant. Lymph nodes were extracted from cattle sourced from seven feedyards. Salmonella prevalence in lymph nodes was found to be 0% in cattle sourced from only one of the seven yards. Lymph nodes from cattle sourced from the other feedyards yielded positive samples, with varying prevalence. Of the remaining six feedyards, one feedyard yielded 88.2% prevalence of Salmonella in bovine lymph nodes, which was significantly higher than all other feedyards (42.9, 40.0, 40.0, 24.0, and 4.0%). The prevalence of Salmonella in the feedlot environment was compared among three feedyards; one yard had 65.0% environmental prevalence of Salmonella, which was statistically higher than the other feedyards surveyed. Of the two remaining yards, one had 0% prevalence of Salmonella in fecal and soil samples, which was also the feedyard with 0% prevalence of Salmonella in lymph nodes. Findings include (1) the significance of effective sanitary dressing procedures and intervention strategies in a beef harvest environment, (2) that there is clear feedyard-to-feedyard variation with relation to Salmonella prevalence in bovine lymph nodes, and (3) that differences in environmental factors existed among feedyards although the reasons remain unclear.