This thesis presents a data-driven approach to constrained control in the form of a subspace-based state-space system identification algorithm integrated into a model predictive controller. Generally this approach has been termed model-free predictive control in the literature. Previous research into this area focused on the system identification aspects resulting in an omission of many of the features that would make such a control strategy attractive to industry. These features include constraint handling, zero-offset setpoint tracking and non-stationary disturbance rejection. The link between non-stationary disturbance rejection in subspace-based state-space system identification and integral action in state-space based model predictive control was shown. Parameterization with Laguerre orthonormal functions was proposed for the reduction in computational load of the controller. Simulation studies were performed using three real-world systems demonstrating: identification capabilities in the presence of white noise and non-stationary disturbances; unconstrained and constrained control; and the benefits and costs of parameterization with Laguerre polynomials.

This dissertation challenges an unstudied area in moving objects database domains; predicting (long-term) future locations of moving objects. Moving object prediction enables us to provide a wide range of applications, such as traffic prediction, pre-detection of an aircraft collision, and reporting attractive gas prices for drivers along their routes ahead. Nevertheless, existing location prediction techniques are limited to support such applications since they are generally capable only of short-term predictions. In the real world, many objects exhibit typical movement patterns. This pattern information is able to serve as an important background to tackle the limitations of the existing prediction methods. We aims at offering foundations of pattern-aware prediction for moving objects, rendering more precise prediction results. Specifically, this thesis focuses on three parts. The first part of the thesis studies the problem of predicting future locations of moving objects in Euclidean space. We introduce a novel prediction approach, termed the hybrid prediction model, which utilizes not only the current motion of an object, but also the object's trajectory patterns for prediction. We define, mine, and index the trajectory patterns with a novel access method for efficient query processing. We then propose two different query processing techniques along given query time, i.e., for near future and for distant future. The second part covers the prediction problem for moving objects in network space. We formulate a network mobility model that offers a concise representation of mobility statistics extracted from massive collections of historical objects trajectories. This model captures turning patterns of the objects at junctions, at the granularity of individual objects as well as globally. Based on the model, we develop three different algorithms for predicting the future path of a mobile user moving in a road network, named the PathPredictors. The third part of the thesis extends the prediction problem for a single object to that for multiple objects. We introduce a convoy query that retrieves all groups of objects, i.e., convoys, from the objects' historical trajectories, each convoy consists of objects that have traveled together for some time; thus they may also move together in the future. We then propose three efficient algorithms for the convoy discovery, called the CuTS family, that adopt line simplification methods for reducing the size of the trajectories, permitting efficient query processing. For each part, we demonstrate comprehensive experimental results of our proposals, which show significantly improved accuracies for moving object prediction compared with state-of-the-art methods, while also facilitating efficient query processing.

Research Doctorate - Doctor of Philosophy (PhD) / This thesis addresses the problem of robustness in model predictive control (MPC) of discrete-time systems. In contrast with most previous work on robust MPC, our main focus is on robustness in the face of both imperfect state information and dynamic model uncertainty. For linear discrete-time systems with model uncertainty described by sum quadratic constraints, we propose output-feedback MPC policies that: (i) treat soft constraints using quadratic penalty functions; (ii) respect hard constraints using 'tighter' constraints; and (iii) achieve robust closed-loop stability and non-zero setpoint tracking. Our two main tools are: (1) a new linear matrix inequality condition which parameterizes a class of quadratic MPC cost functions that all lead to robust closed-loop stability; and (2) a new parameterization of soft constraints which has the advantage of leading to optimization problems of prescribable size. The stability test we use for MPC design builds on well-known results from dissipativity theory which we tailor to the case of constrained discrete-time systems. The proposed robust MPC designs are shown to converge to well-known nominal MPC designs as the model uncertainty (description) goes to zero. Furthermore, the present approach to cost function selection is independently motivated by a novel result linking MPC and minimax optimal control theory. Specifically, we show that the considered class of MPC policies are the closed-loop optimal solutions of a particular class of minimax optimal control problems. In addition, for a class of nonlinear discrete-time systems with constraints and bounded disturbance inputs, we propose state-feedback MPC policies that input-to-state stabilize the system. Our two main tools in this last part of the thesis are: (1) a class of N-step affine state-feedback policies; and (2) a result that establishes equivalence between the latter class and an associated class of N-step affine disturbance-feedback policies. Our equivalence result generalizes a recent result in the literature for linear systems to the case when N is chosen to be less than the nonlinear system's 'input-state linear horizon'.

Thèse (D.Eng.) -- Université du Québec à Chicoutimi, 2007. / La p. de t. porte en outre: Thèse présentée à l'Université du Québec à Chicoutimi comme exigence partielle du doctorat en ingénierie. Bibliogr.: f. 169-181. Document électronique également accessible en format PDF. CaQQUQ

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.)--West Virginia University, 2004. / Title from document title page. Document formatted into pages; contains viii, 104 p. : ill., map. Includes abstract. Includes bibliographical references (p. 101-108).

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.S.)--Oregon State University, 2010. / Printout. Includes bibliographical references (leaves 68-72). Also available on the World Wide Web.

Thesis (Ph. D.)--University of Washington, 1999. / Vita. Includes bibliographical references (leaves 82-87).