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<p> Ten different reduced models, of a particular test system are selected. Two cost functions are selected and the test system minimum cost is found for each. The model optimal controls are found for each cost function and are used to provide sub-optimal control of the system using two different methods. The system cost is calculated for each case and compared to the minimum attainable. The reduction methods are compared with a view to application for the near-optimal control of a linear system.</p> / Thesis / Master of Engineering (MEngr)

Cancer is a generic term for a large group of diseases where cells of the body lose their normal mechanisms for growth so that they grow in an uncontrolled way. One of the most common treatments of cancer is chemotherapy that aims to kill abnormal proliferating cells; however normal cells and other organs of the patients are also adversely affected. In practice, it's often difficult to maintain optimum chemotherapy doses that can maximise the abnormal cell killing as well as reducing side effects. The most chemotherapy drugs used in cancer treatment are toxic agents and usually have narrow therapeutic indices, dose levels in which these drugs significantly kill the cancerous cells are close to the levels which sometime cause harmful toxic side effects. To make the chemotherapeutic treatment effective, optimum drug scheduling is required to balance between the beneficial and toxic side effects of the cancer drugs. Conventional clinical methods very often fail to find drug doses that balance between these two due to their inherent conflicting nature. In this investigation, mathematical models for cancer chemotherapy are used to predict the number of tumour cells and control the tumour growth during treatment. A feedback control method is used so as to maintain certain level of drug concentrations at the tumour sites. Multi-objective Genetic Algorithm (MOGA) is then employed to find suitable solutions where drug resistances and drug concentrations are incorporated with cancer cell killing and toxic effects as design objectives. Several constraints and specific goal values were set for different design objectives in the optimisation process and a wide range of acceptable solutions were obtained trading off among different conflicting objectives. Abstract v In order to develop a multi-objective optimal control model, this study used proportional, integral and derivative (PID) and I-PD (modified PID with Integrator used as series) controllers based on Martin's growth model for optimum drug concentration to treat cancer. To the best of our knowledge, this is the first PID/I-PD based optimal chemotherapy control model used to investigate the cancer treatment. It has been observed that some solutions can reduce the cancer cells up to nearly 100% with much lower side effects and drug resistance during the whole period of treatment. The proposed strategy has been extended for more drugs and more design constraints and objectives.

We investigate the impact of cash reserves upon the optimal behaviour of a modelled firm that has uncertain future revenues. To achieve this, we build up a corporate financing model of a firm from a Real Options foundation, with the option to close as a core business decision maintained throughout. We model the firm by employing an optimal stochastic control mathematical approach, which is based upon a partial differential equations perspective. In so doing, we are able to assess the incremental impacts upon the optimal operation of the cash constrained firm, by sequentially including: an optimal dividend distribution; optimal equity financing; and optimal debt financing (conducted in a novel equilibrium setting between firm and creditor). We present efficient numerical schemes to solve these models, which are generally built from the Projected Successive Over Relaxation (PSOR) method, and the Semi-Lagrangian approach. Using these numerical tools, and our gained economic insights, we then allow the firm the option to also expand the operation, so they may also take advantage of favourable economic conditions.

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No / A novel threefold optimization algorithm is proposed to simultaneously solve the nonlinear model predictive control and dynamic real-time optimization for batch processes while optimizing the batch operation time. Object-oriented programming and parallel computing are exploited to make the algorithm effective to handle industrial cases. A well-known literature case is selected to validate the algorithm.

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In order to have an interception with a target, a missile should be guided with a successful guidance algorithm accompanied with a suitable autopilot structure. In this study, different autopilot and guidance designs for a canard-controlled missile are developed. As a first step, nonlinear missile mathematical model is derived by using the equations of motion with aerodynamic coefficients found by Missile DATCOM program. Autopilot design starts by the linearization of the nonlinear missile model around equilibrium flight conditions. Controllers based on the concepts of optimal control theory results and sliding mode control are designed. In all of the designs, angle of attack command and roll angle command type autopilot structures are used. During the design process, variations in angle of attack, Mach number and altitude can lead to significant performance degradation. This problem is typically solved by applying gain-scheduling methodology according to these parameters. There are different types of guidance methods in the literature. Throughout this study, proportional navigation guidance and its modified forms are selected as a base algorithm in the guidance system design. Other robust forms of guidance methods, such as an optimal guidance approach and sliding mode guidance, are also formed for performance comparison with traditional proportional navigation guidance approach. Finally, a new guidance method, optimal proportional-integral guidance, whose performance is the best among all of the methods included in the thesis against highly maneuvering targets, is introduced.

Wind energy is on an upswing due to climate concerns and increasing energy demands on conventional sources. Wind energy is attractive and has the potential to dramatically reduce the dependency on non-renewable energy resources. With the increase in wind farms there is a need to improve the efficiency in power allocation and power generation among wind turbines. Wake interferences among wind turbines can lower the overall efficiency considerably, while offshore conditions pose increased loading on wind turbines. In wind farms, wind turbines* wake affects each other depending on their positions and operation modes. Therefore it becomes essential to optimize the wind farm power production as a whole than to just focus on individual wind turbines. The work presented here develops a hierarchical power optimization algorithm for wind farms. The algorithm includes a cooperative level (or higher level) and an individual level (or lower level) for power coordination and planning in a wind farm. The higher level scheme formulates and solves a quadratic constrained programming problem to allocate power to wind turbines in the farm while considering the aerodynamic effect of the wake interaction among the turbines and the power generation capabilities of the wind turbines. In the lower level, optimization algorithm is based on a leader-follower structure driven by the local pursuit strategy. The local pursuit strategy connects the cooperative level power allocation and the individual level power generation in a leader-follower arrangement. The leader, could be a virtual entity and dictates the overall objective, while the followers are real wind turbines considering realistic constraints, such as tower deflection limits. A nonlinear wind turbine dynamics model is adopted for the low level study with loading and other constraints considered in the optimization. The stability of the algorithm in the low level is analyzed for the wind turbine angular velocity. Simulations are used to show the advantages of the method such as the ability to handle non-square input matrix, non-homogenous dynamics, and scalability in computational cost with rise in the number of wind turbines in the wind farm.