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The aim of this research project was to develop a position controlled induction machine vector drive operating without a speed or position sensor but having a dynamic performance comparable to that of a sensored position vector drive. The methodology relies on the detection of a rotor saliency in the machine by persistent high-frequency voltage injection. The rotor position is then estimated from the resulting stator current harmonics that are modulated by the spatial rotor saliency. This can be a built-in rotor saliency (a designed asymmetry) or the natural saliency due to rotor slotting. This project investigates the demodulation of the extracted high-frequency current spectrum and different topologies for the estimation of rotor position. The tracking of rotor position through rotor saliencies helps to overcome the limitations of model-based approaches that are restricted to speeds above 30rpm on a 4-pole machine and are sensitive to parameter mismatches. The project addresses the difficult problem of separating the modulation effects due to the rotor saliency from distorting modulations due to the saturation saliency and inverter effects. In previous research it had been found that the saturation saliency causes a deterioration of the position estimate that can result in a loss of position and eventually causes the drive to fail. The application of filters to remove the interfering saturation harmonics is not possible. In this research a new approach was developed that compensates online for the saturation effect using pre-commissioned information about the machine. This harmonic compensation scheme was utilized for a 30kW, 4-pole induction machine with asymmetric rotor and enabled the operation from zero to full load and from standstill up to about ±150rpm (±5Hz). The steady-state performance and accuracy of the resulting sensorless drive has been found to operate similarly to a sensored drive fitted with a medium resolution encoder of 600ppr. The project involved studies of the inverter switching deadtime and its distorting effect on the position estimation. A second compensation strategy was therefore developed that is better suited if a large interfering modulation due to the inverter deadtime is present in the machine. The new compensation method was implemented for a second 30kW machine that utilizes the rotor slotting saliency. Good tracking results were obtained with a mean error of less than ±0.5° mechanical under steady-state. The derivation of the position signal for higher speeds introduces an additional speed-dependent error of about 4° mechanical at 170rpm. Sensorless position control was realized for operation from zero to full load for the fully fluxed machine. The performance allowed low and zero speed operation including position transients reaching a speed of 50rpm. The high-frequency modulation introduced by the fundamental currents during transient operation was examined and identified as the main factor limiting the dynamics of the sensorless drive. Two rigs were used for the research. The first rig is build around a network of Transputers, the second rig uses state-of-the-art TMS320C40 and TMS320F240 digital signal processors for the control and was designed and constructed as part of the research.

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Automated fusion welding processes play a fundamental role in modern manufacturing industries. The proliferation of joint geometries together with the large permutation of associated process variable configurations has given rise to research into complex system modelling and control strategies. Many of these techniques have involved monitoring of not only the electrical characteristics of the process but visual and acoustic information. Acoustic information derived from certain welding processes is well documented as it is an established fact that skilled manual welders utilise such information as an aid to creating an optimum weld. The experimental investigation presented in this thesis is dedicated to the feasibility of monitoring airborne acoustic emissions of Submerged Arc Welding (SAW) for diagnostic and real time control purposes. The experimental method adopted for this research takes a cybernetic approach to data processing and interpretation in an attempt to replicate the robustness of human biological functions. A custom designed audio hardware system was used to analyse signals obtained from bead on mild steel plate fusion welds. Time and frequency domains were used in an attempt to establish salient characteristics or identify the signatures associated with changes of the process variables. The featured parameters were voltage / current and weld travel speed, due to their ease of validation. However, consideration has also been given to weld defect prediction due to process instabilities. As the data proved to be highly correlated and erratic when subjected to off line statistical analysis, extensive investigation was given to the application of artificial neural networks to signal processing and real time control scenarios. As a consequence, a dedicated neural based software system was developed, utilising supervised and unsupervised neural techniques to monitor the process. The research was aimed at proving the feasibility of monitoring the electrical process parameters and stability of the welding process in real time. It was shown to be possible, by the exploitation of artificial neural networks, to generate a number of monitoring parameters indicative of the welding process state. The limitations of the present neural method and proposed developments are discussed, together with an overview of applied neural network technology and its impact on artificial intelligence and robotic control. Further developments are considered together with recommendations for future areas of research.