| Summary: | 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.
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