Telecommunication networks become major parts in modern complex control systems recently. They provide many advantages over conventional point-to-point connections, such as the simplification on installation and maintenance with comparatively low cost and the nature requirement of wireless communication in remote control systems. In practice, limited resource networks are shared by multiple controllers, sensors and actuators, and they may need to serve some other information unrelated to control purpose. Consequently, the control system design in networked control systems should be revised by taking communication constraints, for example, finite precision data, time delay and noise in transmission, into account. This thesis studies the robust control and state estimation of uncertain systems, when feedback information is sent via limited capacity communication channels. It focuses on the problem of finite precision data due to the communication constraints. The proposed schemes are based on the robust set-valued state estimation and the optimal control techniques. A state estimation problem of linear uncertain system is studied first. In this problem, we propose an algorithm called coder-decoder for uncertain systems. The coder encodes the observed output into a finite-length codeword and sends it to the decoder that generates the estimated state based on the received codeword. As an illustration, we apply the results in state estimation problem to a precision missile guidance problem using sensor fusion. In this problem, the information obtained from remote sensors is transmitted through limited capacity communication networks to the guided missile. Next, we study a stabilization problem of linear uncertain systems with state feedback. In this problem, the coder-controller scheme is developed to asymptotically stabilize the uncertain systems via limited capacity communication channels. The coder encodes the full state variable into a finite-length codeword and sends it to the controller that drives the system state to the origin. To achieve the asymptotic stability, we use a dynamic quantizer so that quantization noise converges to zero. The results in both state estimation and stabilization problems can handle the problem of finite data rate communication networks in control systems.

A Persistent Node is a redundant distributed mechanism for storing a key/value pair reliably in a geographically local network. In this paper, I develop a method of establishing Persistent Nodes in an amorphous matrix. I address issues of construction, usage, atomicity guarantees and reliability in the face of stopping failures. Applications include routing, congestion control, and data storage in gigascale networks.

This thesis proposes a new method to compute the lower and upper bounds of solution to the generalized Lyapunov matrix equation. Convergence of the iteratively computed bounds is illustrated by several numerical examples. Moreover, regularization of the condition required in computing the upper bound is also investigated. Finally, the method is employed to derive a less conservative bound on the magnitude of perturbation without destroying the stability of the perturbed system. All the theoretical results are verified by the numerical examples.

This thesis mainly studies the active power factor correction circuit using a new AC/DC Single Ended Primary Inductance Converter (SEPIC). For power factor correction, inductor current is operated in the continuous conduction mode. First of all, the converter is analyzed by state space averaging method. Furthermore, the operational principle of PFC circuit with PI control law is analyzed. A good power factor system is then developed by time-domain and frequency-domain analysis. A classical PFC circuit with PI control law has low power factor when light load. In order to overcome problem, the thesis proposes a SEPIC circuit with robust performance. Compared with circuits using classical PI controller and PFC IC, the proposed system obtains higher power factor under the condition of the same light load.

The goal when applying robust engineering design methods is to improve a system's quality by reducing its sensitivity to uncertainty that has influence on the performance of the product. In the Robust Concept Exploration Method (RCEM) this approach is facilitated with additionally giving the designer the possibility to search for a compromise between the desired performance and a satisfying robustness. The current version of the RCEM, however, has some limitations that render it inapplicable for nonlinear design problems. These limitations, which are demonstrated in this thesis, are mainly connected to the application of global response surfaces and the Taylor series for variance estimations. In order to analyze the limitation of the robustness estimation, several alternative methods are developed, assessed and introduced to a modified RCEM. The developed Multiple Point Method is based on the Sensitivity Index (SI) and improves the variance estimation in RCEM significantly, especially for nonlinear problems. This approach is applicable to design problems, for which the performance functions are known explicitly. For problems that require simulations for the performance estimation, the simulation-based RCEM is developed by introducing the Probabilistic Collocation Method (PCM) to robust concept exploration. The PCM is a surrogate model approach, which generates local response models around the points of interests with a minimum number of simulation runs. Those models are utilized in the modified-RCEM for the uncertainty analysis of the system's performance. The proposed methods are tested with two examples each. The modified RCEM is validated with an artificial design problem as well as the design of a robust pressure vessel. The simulation-based RCEM is validated using the same artificial design problem and the design of a robust multifunctional Linear Cellular Alloy (LCA) heat exchanger for lightweight applications such as mobile computing. The structure of the theoretical and empirical validation of the methods follows the validation square.

Thesis (Ph. D.)--West Virginia University, 2001. / Title from document title page. Document formatted into pages; contains x, 121 p. : ill. (some col.). Vita. Includes abstract. Includes bibliographical references (p. 99-103).

Thesis (M. Phil.)--University of Hong Kong, 1987.

published_or_final_version / Electrical and Electronic Engineering / Doctoral / Doctor of Philosophy

Cross-docking is an important transportation logistics strategy in supply chain management which reduces transportation costs, inventory holding costs, order-picking costs and response time. Careful planning is needed for successful cross-dock operations. Uncertainty in cross-dock problems is inevitable and needs to be addressed. Almost all research in the cross-dock area assumes determinism. This dissertation considers uncertainty in cross-dock problems and optimizes these problems under uncertainty. We consider uncertainty in processing times, using scenario-based and protection-based robust approaches. Using a heuristic method, we find a lower and upper bound and combine that with a meta-heuristic method to solve the problem. Also, we consider problems in two different industries (Goodwill and H-E-B) and address the uncertainties that happen frequently in their operations. The scenario-based robust optimization model for the unloading problem using a min max objective is presented with examples. A surrogate heuristic procedure is used to find a robust solution. Next, a two-space genetic algorithm, a meta-heuristic procedure, is applied to the unloading problem using the bounds obtained by the heuristic procedure. The results are closer to the optimal solution than those obtained by the two-space genetic algorithm without bounds. When compared with the regular genetic algorithm with bounds, the two-space algorithm performs well. The protection-based approach considers a limit on the number of coefficients allowed to change with data uncertainty, protecting against the degree of conservatism. The management of trucks and reduction of overtime pay in the cross-dock operations of Goodwill is addressed through two models and uncertainty is applied to those models. A combined cross-dock operations model together with demand is formulated and the uncertainties are discussed for H-E-B operations. This dissertation does not address the recycling operation within the cross-dock of Goodwill, or the uncertainty in H-E-B data. / text

published_or_final_version / Statistics / Master / Master of Philosophy