Thesis (M. S.)--Aerospace Engineering, Georgia Institute of Technology, 2005. / Bottasso, Carlo, Committee Chair ; Hodges, Dewey, Committee Member ; Bauchau, Olivier, Committee Member. Includes bibliographical references.

Thesis (Ph. D.)--University of Missouri-Columbia, 2002. / There are no leaves 76-78; manuscript misnumbered between 75 and 79. Typescript. Vita. Includes bibliographical references. Also available on the Internet.

Thesis (Ph. D.)--University of Missouri-Columbia, 2002. / There are no leaves 76-78; manuscript misnumbered between 75 and 79. Typescript. Vita. Includes bibliographical references. Also available on the Internet.

Thesis (M. Sc.)(Electronic Engineering)--University of Pretoria, 2006. / Includes bibliographical references. Available on the Internet via the World Wide Web.

<p> The topic of study within includes the development and application of nonlinear control technologies on sampled systems. Discrete control structures are introduced that expand on existing differential geometric and predictive control methods. The differential geometric techniques are described from the error trajectory context, which are typically only derived for continuous application. The discrete error trajectory controllers introduced have one of two configurations. The first configuration requires satisfaction of the error trajectory objective at the next sampling interval through prediction of system behaviour over the controller sampling interval. This objective found limited success and it is observed that satisfaction of the error trajectory objective at discrete intervals does not generally result in the intended response. The second configuration minimizes the integrated distance from the error manifold defined by the error trajectory objective over the entire controller sampling interval. It is observed that this integrated error trajectory controller best emulates the intent of the continuous controller in the discrete domain. Techniques borrowed from predictive control are incorporated into the integrated error trajectory controller such as input move suppression and constraints to produce an optimal error trajectory controller, further improving performance.</p> <p> The predictive control method introduced utilizes a transformation of the input space. The differentiating property of input transformation predictive control (ITPC) from other methods is the prediction technique that is capable of estimating the future behaviour of nonlinear systems through elementary matrix operations similar to the dynamic matrix control (DMC) prediction technique. This is achieved by separation of the steady state and dynamic system properties and the introduction of an intermediate state prediction layer. This allows for the nonlinear prediction of system behaviour without the need to numerically integrate the system model.</p> <p> Two example systems are used to demonstrate application of the discrete error trajectory and ITPC on nonlinear controllers. Performance for these control structures is compared to technologies accepted within the control community for a broad range for characteristics including, computation efficiency, design effort and other nonlinear performance criteria, with favourable results.</p> / Thesis / Master of Engineering (MEngr)

Thesis (Ph. D.)--University of Missouri-Columbia, 1999. / Typescript. Vita. Includes bibliographical references (leaves [217]-221). Also available on the Internet.

A multivariable control technique is proposed for a type of nonlinear system with parameter intervals. The control is based upon the feedback linearization scheme called Generic Model Control, and alters the control calculation by utilizing parameter intervals, employing an adaptive step, averaging control predictions, and applying an interval problem solution. The proposed approach is applied in controlling both a linear and a nonlinear arc welding system as well in other simulations of scalar and multivariable systems.

Thesis (Ph.D.(Electronic Engineering))--University of Pretoria, 2009. / Summaries in Afrikaans and English. Includes bibliographical references.

Thesis (Ph. D.)--University of Missouri-Columbia, 1999. / Typescript. Vita. Includes bibliographical references (leaves [217]-221). Also available on the Internet.

Model predictive control (MPC) for switched reluctance machine (SRM) drives is studied in this thesis. The objective is to highlight the benefits of implementing MPC to overcome the main drawbacks of SRMs and position them as an attractive alternative among electrical drives. A comprehensive literature review of MPC for SRM is presented, detailing its current trends as an application still at an early stage. The different features of MPC are highlighted and paired with the most challenging and promising control objectives of SRMs. A vision of future research trends and applications of MPC-driven SRMs is proposed, thus drawing a road-map of future projects, barriers to overcome and potential developments. Several important applications can take advantage of the improved features that SRM can get with MPC, especially from the possibility of defining a unified control technique with the flexibility to adapt to different system requirements. The most important cluster for SRM drives is the high- and ultrahigh-speed operative regions where conventional machines cannot work efficiently. SRMs with MPC can complement then the existing demand for electrical drives with high performance under challenging conditions. Three techniques based on the finite control set model predictive control (FCS-MPC) approach are developed out of the proposed road-map. The first one defines a virtual-flux current tracking technique that improves the existing ones in operating at different speeds and more than one quadrant operation. The method is validated for low- and high- power SRMs in simulations and diverse types of current waveform, making it easy to adapt to existing current shaping techniques. It is also validated experimentally for different operating conditions and robustness against parameter variation. The second technique proposed a predictive torque control that bases its model on static-maps, thus avoiding complex analytical expressions. It improves its estimation through a Kalman filter. The third technique uses a virtual-flux predictive torque control, similar to the first technique for current tracking. The techniques are validated at a wide speed range, thus evidencing superiority in performance without modification on the control structure. / Thesis / Doctor of Philosophy (PhD)