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No description available.

No description available.

No description available.

No description available.

No description available.

No description available.

Multi-dimensional (n-D) systems can be described by matrices whose elements are polynomial in more than one indeterminate. These systems arise in the study of partial differential equations and delay differential equations for example, and have attracted great interest over recent years. Many of the available results have been developed by generalising the corresponding results from the well known 1-D theory. However, this is not always the best approach since there are many differences between 1-D, 2-D and n-D (n > 2) polynomial matrices. This is due mainly to the underlying polynomial ring structure.

<p> In this thesis a non-parametric normalized stochastic approximation algorithm has been developed for the identification of multivariable systems from noisy data without prior knowledge of the statistics of measurement noise.</p> <p> The system model is first transformed into a special canonical form, then it is formulated in a non-parametric form. The parameters of this model are estimated through a normalized stochastic approximation algorithm. Finally, the system parameters are recovered from these estimates by another transformation.</p> <p> The proposed algorithm is applied to the identification of two simulated systems.</p> <p> Conclusions of this work and suggestions for future work are given.</p> / Thesis / Master of Engineering (MEngr)

<p> This study is concerned with the multivariable stochastic regulatory control of a pilot plant fixed bed reactor which is interfaced to a minicomputer. The reactor is non-adiabatic with a highly exothermic, gaseous catalytic reaction, involving several independent species. A low order state space model for the reactor is developed starting from the partial differential equations describing the system. A parameter estimation method is developed to fit the model to experimental data. Noise disturbances present in the system are identified using two methods, and two alternative dynamic-stochastic state space models are obtained. Multivariable stochastic feedback control algorithms are derived from these models and are implemented on the reactor in a series of DDC control studies. The control algorithms are compared with each other and with a single loop controller. The best of the multivariable control algorithms is used to regulate the exit concentrations of the various species from the reactor and the results are compared to data.</p> / Thesis / Doctor of Philosophy (PhD)