Thesis (M. Phil.)--University of Hong Kong, 2007. / Title proper from title frame. Also available in printed format.

Thesis (Ph.D.)--University of Melbourne, Dept. of Computer Science and Software Engineering, 2009. / Typescript. Includes bibliographical references (p. 179-198)

Thesis (Ph. D.)--University of Missouri-Columbia, 2005. / The entire dissertation/thesis text is included in the research.pdf file; the official abstract appears in the short.pdf file (which also appears in the research.pdf); a non-technical general description, or public abstract, appears in the public.pdf file. Title from title screen of research.pdf file viewed on (November 14, 2006) Vita. Includes bibliographical references.

The next generation of U.S. Navy ships will see the integration of the propulsion and electrical systems as part of the all-electric ship. This new architecture brings advantages and challenges. One of the challenges is to develop a stable power system that can ride through various issues such as faults or changes in load. style='mso-spacerun:yes'> While terrestrial systems have been studied for a long time related to stability, the unique characteristics of the shipboard power system mean that not all of these results are directly applicable to the all-electric ship. Because of the new shipboard power system structure, more generators are required to be connected in parallel to supply the power needed. Control of parallel generators has been done for years in terrestrial systems; however, the application of an advanced control technique has not been applied in the All-Electric Ship. The challenge is to apply an advanced control technique to the all-electric shipboard power system that will maintain stability of multiple generator systems, keeping in mind that the generators could be dissimilar in ratings. style='mso-spacerun:yes'> For that reason, the control techniques used to solve the problem need to be developed or adapted for test cases that are similar to the electric ship configuration. This dissertation provides a description of an effort to implement a robust control scheme on the all-electric ship. style='mso-spacerun:yes'> The proposed solution is to apply H∞ Robust Control as an advanced control technique, with realistic constraints to keep the shipboard power system within stability margins during normal and abnormal operating scenarios. In this work, H∞ Robust Control has been developed in the form of state space equations which are optimized using linear matrix implementation. The developed H∞ Control has been implemented on the different operating scenarios to validate the functionality and to compare it with another control technique. style='mso-spacerun:yes'> Test case results for one-generator, two-generator similar and two-generator dissimilar have been described. style='mso-spacerun:yes'> Stability indicators have been determined and compared for various types of faults and transients for removing and adding static and dynamic loads. The research provides the foundation for applications of advanced control techniques for the next generation all-electric ship.

No description available.

In this thesis we demonstrate a technique for robust controller design for an autonomous underwater vehicle (AUV) that explicitly handles the trade-off between reference tracking, agility, and energy efficient performance. AUVs have many sources of modeling uncertainty that impact the uncertainty in maneuvering performance. A robust control design process is proposed to handle these uncertainties while meeting control system performance objectives. We investigate the relationships between linear system design parameters and the control performance of our vehicle in order to inform an H∞ controller synthesis problem with the objective of balancing these tradeoffs. We evaluate the controller based on its reference tracking performance, agility and energy efficiency, and show the efficacy of our control design strategy. / Master of Science / In this thesis we demonstrate a technique for autopilot design for an autonomous underwater vehicle (AUV) that explicitly handles the trade-off between three performance metrics. Mathematical models of AUVs are often unable to fully describe their many physical properties. The discrepancies between the mathematical model and reality impact how certain we can be about an AUV's behavior. Robust controllers are a class of controller that are designed to handle uncertainty. A robust control design process is proposed to handle these uncertainties while meeting vehicle performance objectives. We investigate the relationships between design parameters and the performance of our vehicle. We then use this relationship to inform the design of a controller. We evaluate this controller based on its energy efficiency, agility and ability to stay on course, and thus show the effectiveness of our control design strategy.

No description available.

Solar Wireless Networked Control Systems (SWNCS) are a style of distributed control systems where sensors, actuators, and controllers are interconnected via a wireless communication network. This system setup has the benefit of low cost, flexibility, low weight, no wiring and simplicity of system diagnoses and maintenance. However, it also unavoidably calls some wireless network time delays and packet dropout into the design procedure. Solar lighting system offers a clean environment, therefore able to continue for a long period. SWNCS also offers multi Service infrastructure solution for both developed and undeveloped countries. The system provides wireless controller lighting, wireless communications network (WI-FI/WIMAX), CCTV surveillance, and wireless sensor for weather measurement which are all powered by solar energy.

published_or_final_version / abstract / Electrical and Electronic Engineering / Master / Master of Philosophy

In the chemical or biochemical industry most processes are modeled by nonlinear equations. It is of a great significance to design high-performance nonlinear controllers for efficient control of these nonlinear processes to achieve closed-loop system's stability and high performance. However, there are many difficulties which hinder the design of such controllers due mainly to the process nonlinearity. In this work, comprehensive design procedures based on robust control have been proposed to efficiently deal with the design of gain-scheduled controllers for nonlinear systems. Since all the design procedures proposed in this work rely strongly on the process model, the first difficulty addressed in this thesis is the identification of a relatively simple model of the nonlinear processes under study. The nonlinearity of the processes makes it often difficult to obtain a first-principles model which can be used for analysis and design of the controller. As a result, relatively simple empirical models, Volterra series model and state-affine model, are chosen in this work to represent the nonlinear process for the design of controllers. The second major difficulty is that although the nonlinear models used in this thesis are easy to identify, the analysis of stability and performance for such models using nonlinear control theory is not straightforward. Instead, it is proposed in this study to investigate the stability and performance using a robust control approach. In this approach, the nonlinear model is approximated by a nominal linear model combined with a mathematical description of model error to be referred to, in this work, as model uncertainty. In the current work it was assumed that the main source of uncertainty with respect to the nominal linear model is due to the system nonlinearity. Then, in this study, robust control theoretical tools have been especially developed and applied for the design of gain-scheduled Proportional-Integral (PI) control and gain-scheduled Model Predictive Control (MPC). Gain-scheduled controllers are chosen because for nonlinear processes operated over a wide range of operation, gain-scheduling has proven to be a successful control design technique (Bequette, 1997) for nonlinear processes. To guarantee the closed-loop system's robust stability and performance with the designed controllers, a systematic approach has been proposed for the design of robust gain-scheduled controllers for nonlinear processes. The design procedure is based on robust stability and performance conditions proposed in this work. For time-varying uncertain parameters, robust stability and performance conditions using fixed Lyapunov functions and parameter-dependent Lyapunov functions, were used. Then, comprehensive procedures for the design and optimization of robust gain-scheduled PI and MPC controllers tuning parameters based on the robust stability and performance tests are then proposed. Since the closed-loop system represented by the combination of a state-affine process model and the gain-scheduled controller is found to have an affine dependence on the uncertain parameters, robust stability and performance conditions can be tested by a finite number of Linear Matrix Inequalities (LMIs). Thus, the final problems are numerically solvable. One of the inherent problems with robust control is that the design is conservative. Two approaches have been proposed in this work to reduce the conservatism. The first one is based on parameter-dependent Lyapunov functions, and it is applied when the rate of change of the time-varying uncertainty parameters is <i>a priori</i> available. The second one is based on the relaxation of an input-saturation factor defined in the thesis to deal with the issue of actuator saturation. Finally, to illustrate the techniques discussed in the thesis, robust gain-scheduled PI and MPC controllers are designed for a continuous stirred tank reactor (CSTR) process. A simple MIMO example with two inputs and two outputs controlled by a multivariable gain-scheduled MPC controller is also discussed to illustrate the applicability of the methods to multivariable situations. All the designed controllers are simulated and the simulations show that the proposed design procedures are efficient in designing and comparing robust gain-scheduled controllers for nonlinear processes.