This thesis reports the successful application of the recently introduced Generalised Predictive Control self-tuner to the high-performance positioning of a real flexible single-link robot arm. The large amount of experimental time available on this high bandwidth system allowed exhaustive testing of the 'tuning-knobs' and 'design-filters' available to the user for tailoring the closed-loop. Based upon these experiments a coherent philosophy for configuring GPC in practice is generated. Configuration details found to be necessary for satisfactory GPC control of this high-order neutrally stable and non-minimum-phase plant, with its lightly damped resonant modes, are isolated. In particular it is found that band-pass filtering of data is essential for stable offset-free control using finite-order models of the plant. These aspects are considered in detail both theoretically and experimentally. In this application, as is often the case in practice, some information about the plant dynamics is available beforehand. Novel methods for the inclusion of this prior knowledge are introduced and their beneficial effects on the convergence of the recursive least squares estimation scheme, upon which most self-tuners are based, are demonstrated in simulation and experiment. A novel high-speed 68010/20 multi-processor computer system is described which allows the implementation of GPC at the required high sample rate (60Hz). The software division of the self-tuning algorithm into concurrently and sequentially executing tasks is discussed in detail.

This thesis is concerned with suboptimal adaptive control of discrete linear stochastic processes whose parameters are unknown. The suboptimal adaptive controllers considered are (i) Open Loop Feedback Optimal (OLFO) controller, (ii) self-tuning controller, and (iii) optimal k step ahead controller. Two more controllers, certainty about parameter (CAP) controller and no learning (NOL) controller, that provide bounds on the performance of these adaptive controllers are also considered. Performance of these controllers have been evaluated for a first order process through monte-carlo simulations. Simulation of OLFO controller together with the bounding controllers for the first order process when there is only one unknown parameter revealed that OLFO controller is unsuitable to control unstable processes and would be an unwise choice even for controlling stable processes. Selftuning and OK controllers have been simulated for the first order process with all the parameters unknown. Three cases for the unknown parameters have been considered. They are: (i) constant unknown parameters (ii) slowly time-varying unknown parameters and (iii) rapidly time-varying unknown parameters. Simulation results showed that in certain regions of the unknown parameter space the cost produced by self tuning controller and OK controller are very similar, in certain regions the OK controller produces lesser cost than the self-tuning controller and in certain other regions both controllers perform very badly. But self-tuning controller always took only half as much computing time as OK controller. A necessary condition for convergence of OK controller to a linear constant parameter controller having the same functional form as CAP controller is found out using the ideas of uniform complete observability. For a first order process under OK controller the only occasion the condition would be violated is when there is 'turn-off'. Finally, it is shown that using the combined state/parameter estimator in the place of extended Kalman filter the computational requirement of OK controller can be reduced. For the first order process, OK controller with the combined estimator took only sixty percent as much computing time as the OK controller with extended Kalman filter without any appreciable deterioration in the performance.

The thesis describes Load Frequency Control techniques which may be used for real-time on-line control of large electrical power systems. Traditionally the frequency control of power systems has been carried out using standard fixed parameter control schemes, which give control over the immediate steady- state error and the long term accumulated frequency error, but do not account for the fact that system conditions can alter due to the change in consumer load and generating patterns. The thesis presents a method of controlling the system frequency using adaptive control techniques, which ensure that optimal control action is calculated based on the present system conditions. It enables the system operating point to be monitored so that optimal control may continue to be calculated as the system operating point alters. The proposed method of frequency control can be extended to meet the problems of system interconnection and the control of inter-area power flows. The thesis describes the work carried out at Durham on a fixed parameter control scheme which led to the development of an adaptive control scheme. The controller was validated against a real-time power system simulator with full Energy Management software. Results are also presented from work carried out at the Central Electricity Research Laboratories under the C.A.S.E award scheme. This led to the development of a power system simulator, which along with the controller was validated on-line with the Dispatch Project used by the Central Electricity Generating Board.

The increasing complexity of modern power systems has led to a greater dependence on automatic control at all levels of operation. Large scale systems of which a power system is a prime example, is an area in which a wide gap exists between theoretical mathematically based research and engineering practice. The research programme at Durham is directed towards bridging this gap by linking some of the available and new theoretical techniques with the practical requirements of on-line computer control in power systems. This thesis is concerned with the assessment of security of power systems in real-time operation. The main objective of this work was to develop a package to be incorporated in the University of Durham On line Control of Electrical Power Systems (OCEPS) suite to cater for network islanding and analyse the features and the feasibility of a real-time 'security package’ for modern energy control centres. The real-time power systems simulator developed at Durham was used to test the algorithms and numerical results obtained are presented.

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.

No description available.

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.