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A comprehensive and novel system of automatic control has been designed and constructed using distributed intelligence techniques to control a complex item of equipment for the measurement of linear thermal expansion over the temperature range 1.5K - 300K. The system is designed to perform its own self-calibration automatically before the experiment commences. The low temperature dllatometer developed for this research project incorporates the most sensitive length change sensor available; a three-terminal capacitance transducer. This transducer technique has been refined to resolve length changes of 10-5A and more importantly, measure length against temperature profiles to better than 10-2A. The period of time required to collect a full set of data measurements on any particular specimen was in excess of 100 hours. Forming the heart of the automated control system is an assembly of Intel MCS-Sl single chip microcontrollers connected together on a serial link consisting of a simple pair of wires. The approach has been to divide the system automation into a number of specific control tasks and to allocate a different task to each controller. The Instrument has been used as a diagnostic tool to investigate the properties of lead glasses, and in particular to study the possibility of negative thermal expansion existing at low temperatures. Thermal expansion measurements were also performed to observe the phase change within single crystals of polydiacetylene and to research into low temperature phenomena occurlng within the crystal.

Multi-actuator structural testing has traditionally been regarded, from a control point of view, as a multi-loop single-input, single-output problem. This approach does not take into account the interaction between. different actuators, due to the dynamics of the structure under test, which can be considerable. The result of this is often poor laboratory reproduction of the actual service data. This project shows that the mass of the structure under test has a considerable impact upon the stability of the traditional multi-loop, single-input, single-output control system. Where stability is prejudiced, the loop gains have to be reduced to maintain stability and this can degrade the performance of the test. In these circumstances multivariable control offers the potential for a significant improvement in performance. Two experimental rigs are used in this project, both exhibit major interaction and pose a significant control problem. The first rig consists of a laboratory scale cantilever beam excited by two electro-dynamic vibrators with displacements measured by Linear Variable Differential Transformers (L VDTs). The second, industrial-scale, rig consists of a large steel frame excited by two hydraulic actuators with applied force measured by load cells. Multivariable controllers are designed and implemented on these rigs based on the frequency-domain Characteristic Locus method. The multivariable controllers are shown to demonstrate superior performance to traditional multi-loop controllers. Mathematical models of the rigs are not required for controller design, instead experimental frequency responses are all that are needed. This is a major attraction of the Characteristic Locus method since the task.of modelling the dynamics of a multichannel structural test system is not trivial. However, obtaining the frequency response of the second rig is made difficult by the imposition of closed-loop control during the identification experiment. A technique is presented to overcome this problem using an existing correlation method.

Since the construction of the first electronic computer, forty years ago, reliability has been an important consideration in the minds of computer designers. However, the available technology limited early efforts at fault-tolerance, error-checking and fault recovery. The early valve and discrete transistor machines tended to be large and heavy, unsuitable for applications such as on-board automatic train control, even with no error detection and safety systems. With improvements in component technology, vehicle-borne control electronics has become a practical reality, but until the emergence of large scale integrated circuits (LSI) constraints on fault detection systems remained. The review sections of this thesis trace the development of high-reliability computing techniques, in particular those applied to vehicle control. Control designs are becoming more ambitious, and as microprocessors rival minicomputers for speed and instruction set power, multiprocessing at low cost is easily attainable. In this atmosphere of cheap computer power, the British Rail Automatic Train Operation (ERATO) project was born. Seven microprocessor systems were to run three programs to effect (almost) driverless train control and more. The research for this thesis involved examining the alternative methods for tackling the problems of safety and availability in a train-borne system. The design and development of a multiprocessing module, Cyclone I will be described. Cyclone I contains three l6 bit microprocessors and three program stores and runs as a compact self-checking multiprocessor. When fully developed, the design will be suitable not only for train systems, but for other applications requiring a high confidence level in the output control signals.

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This thesis documents the work that has been done towards the development of a 'practical' self-tuning controller for turbine generator plant. It has been shown by simulation studies and practical investigations using a micro-alternator system that a significant enhancement in the overall performance in terms of control and stability can be achieved by improving the primary controls of a turbine generator using self-tuning control. The self-tuning AVR is based on the Generalised Predictive Control strategy. The design of the controller has been done using standard off-the-shelf microprocessor hardware and structured software design techniques. The proposed design is thus flexible, cost-effective, and readily applicable to 'real' generating plant. Several practical issues have been tackled during the design of the self-tuning controller and techniques to improve the robustness of the measurement system, controller, and parameter estimator have been proposed and evaluated. A simple and robust measurement system for plant variables based on software techniques has been developed and its suitability for use in the self-tuning controller has been practically verified. The convergence, adaptability, and robustness aspects of the parameter estimator have been evaluated and shown to be suitable for long-term operation in 'real' self-tuning controllers. The self-tuning AVR has been extensively evaluated under normal and fault conditions of the turbine generator. It has been shown that this new controller is superior in performance when compared with a conventional lag-lead type of fixed-parameter digital AVR. The use of electrical power as a supplementary feedback signal in the new AVR is shown to further improve the dynamic stability of the system. The self-tuning AVR has been extended to a multivariable integrated self-tuning controller which combines the AVR and EHG functions. The flexibility of the new AVR to enable its expansion for more complex control applications has thus been demonstrated. Simple techniques to incorporate constraints on control inputs without upsetting the loop decoupling property of the multivariable controller have been proposed and evaluated. It is shown that a further improvement in control performance and stability can be achieved by the integrated controller.

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No description available.