Genetic algorithms (GAs) are stochastic search techniques inspired by the principles of natural selection and natural genetics which have revealed a number of characteristics particularly useful for applications in optimization, engineering, and computer science, among other fields. In control engineering, they have found application mainly in problems involving functions difficult to characterize mathematically or known to present difficulties to more conventional numerical optimizers, as well as problems involving non-numeric and mixed-type variables. In addition, they exhibit a large degree of parallelism, making it possible to effectively exploit the computing power made available through parallel processing. Despite their early recognized potential for multiobjective optimization (almost all engineering problems involve multiple, often conflicting objectives), genetic algorithms have, for the most part, been applied to aggregations of the objectives in a single-objective fashion, like conventional optimizers. Although alternative approaches based on the notion of Pareto-dominance have been suggested, multiobjective optimization with genetic algorithms has received comparatively little attention in the literature. In this work, multiobjective optimization with genetic algorithms is reinterpreted as a sequence of decision making problems interleaved with search steps, in order to accommodate previous work in the field. A unified approach to multiple objective and constraint handling with genetic algorithms is then developed from a decision making perspective and characterized, with application to control system design in mind. Related genetic algorithm issues, such as the ability to maintain diverse solutions along the trade-off surface and responsiveness to on-line changes in decision policy, are also considered. The application of the multiobjective GA to three realistic problems in optimal controller design and non-linear system identification demonstrates the ability of the approach to concurrently produce many good compromise solutions in a single run, while making use of any preference information interactively supplied by a human decision maker. The generality of the approach is made clear by the very different nature of the two classes of problems considered.

The development of adaptive control design techniques for nonlinear systems with parametric uncertainty has been intensively studied in recent years. The recently developed adaptive backstepping technique has provided a systematic solution to the problem of designing static adaptive controllers for uncertain nonlinear systems transformable into the triangular Parametric Strict Feedback and Parametric Pure Feedback forms. The adaptive backstepping technique has been adopted in this thesis as the control design approach and a number of new algorithms have been developed for the design of dynamical controllers for the regulation and tracking of deterministic and adaptive control systems. The combination of adaptive backstepping and Sliding Mode Control has also been proposed to design robust adaptive strategies for uncertain systems with disturbances. The class of adaptive backstepping nonlinear systems has been broadened to observable minimum phase systems which are not necessarily transformable into tri- angular forms. The design of output feedback control, when only the output is measured, has also been studied for a class of uncertain systems transformable into the adaptive generalized observer canonical form. Since the equations arising from these new algorithms are too complicated to be computed by hand, a symbolic algebraic toolbox has been developed. This toolbox implements the proposed algorithms for the design of static (dynamic) deterministic (adaptive) controllers, and automatically generates MATLAB code programs for computer simulation.

This study describes the design optimization of permanent actuators, of both rotary and linear topologies. Parameter scanning, constrained single and multi-criterion optimization techniques are developed, with due emphasis on the efficient determination of optimal designs. The modelling of devices by non-linear lumped reluctance networks is considered, with particular regard to the level of discretization required to produce accurate global quantities. The accuracy of the lumped reluctance technique is assessed by comparison with non-linear finite element analysis. Alternative methods of force/torque calculation are investigated, e.g. Lorentz equation, Virtual Work, and Maxwell Stress Integration techniques, in order to determine an appropriate technique for incorporation in a non-linear iterative optimization strategy. The application of constrained optimization in a design environment is demonstrated by design studies and experimental validation on selected prototype devices of both topologies.

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Currently small vessels use autopilots based on the Proportional plus Integral plus Derivative (PID) algorithm which utilises fixed gain values. This type of autopilot is known to often cause performance difficulties, a survey is therefore carried out to identify the alternative autopilot methods that have been previously investigated. It is shown that to date, all published work in this area has been based on large ships, however, there are specific difficulties applicable to the small vessel which have therefore not been considered. After the recognition of artificial neural networks and fuzzy logic as being the two most suitable techniques for use in the development of a new, and adaptive, small vessel autopilot design, the basic concepts of both are reviewed and fiizzy logic identified as being the most suitable for this application. The remainder of the work herein is concerned with the development of a fuzzy logic controller capable of a high level of performance in the two modes of coursekeeping and course-changing. Both modes are integrated together by the use of nonlinear fuzzy input windows. Improved performance is then obtained by using a nonlinear fuzzy rulebase. Integral action is included by converting the fuzzy output window to an unorthodox design described by two hundred and one fuzzy singletons, and then by shifting the identified fuzzy sets to positive, or negative, in order that any steady-state error may be removed from the vessel's performance. This design generated significant performance advantages when compared to the conventional PID autopilot. To develop further into an adaptive form of autopilot called the self-organising controller, the single rulebase was replaced by two enhancement matrices. These are novel features which are modified on-line by two corresponding performance indices. The magnitude of the learning was related to the observed performance of the vessel when expressed in terms of its heading error and rate of change of heading error. The autopilot design is validated using both simulation, and full scale sea trials. From these tests it is demonstrated that when compared to the conventional PID controller, the self-organising controller significantly improved performance for both course-changing and course-keeping modes of operation. In addition, it has the capability to learn on-line and therefore to maintain performance when subjected to vessel dynamic or environmental disturbance alterations.

The design and evaluation of a control system, which can be utilised for the automatic guidance of large ships in confined or restricted waters, is investigated. The vessel is assumed to be a multivariable system and it is demonstrated that a non-linear, time-varying mathematical model most accurately describes the motion of the hull, particularly in tight manoeuvres. A discrete optimal controller has been designed to control simultaneously track, heading and forward velocity. The system is most effective whilst operating under a dual-mode policy. It is shown that feedback matrix adaption is necessary to deal with changes in forward velocity and a form of gain scheduling is proposed. Active disturbance control is employed to counteract effects of wind and tide. An inertial navigation system, together with an optimal controller and filter, is installed on-board a car ferry model. Free-sailing tests show that the performance characteristics of the system are in accordance with theoretical predictions. The feasibility of implementation on a full-size vessel is considered.

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