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Submission note: A thesis submitted in total fulfillment for the degree of Doctor of Philosophy to the Department of Electronic Engineering, Faculty of Science, Technology and Engineering, La Trobe University.This thesis presents a novel sensorless position technique for a Switched Reluctance Motor (SRM). The widespread use of SRMs has been relatively limited when compared to other popular motor technologies. Renewed interest has occurred in SRMs due to low cost, environmentally friendly materials and its wide torque operating region. One of the disadvantages affecting the wider use of an SRM, is the requirement for a rotor position sensor. This increases the cost and reduces the durability of the system. As a result, much research has been dedicated to making the motor `sensorless'. The proposed sensorless technique seeks to improve the operating range of currently available methods, with the advantage that there is no need for an offline calibration technique. To achieve sensorless operation, the proposed technique evaluates the inductance waveform on a per phase basis, to find the minimum and maximum inductance positions. These locations correspond to the unaligned and aligned rotor positions respectively. A sensorless rotor position is created via a novel digital phase locked loop (DPLL), which generates intermediate positions between these detected positions. To enhance sensorless tracking and accuracy a multiple model adaptive estimator Kalman filter is introduced. This improves the position estimates of the un-aligned and aligned rotor positions and improves the sensorless tracking output of the DPLL. Lastly, to ensure sensorless operation from zero speed to normal sensorless operation, a novel hesitation-free starting technique is presented. The performance of the proposed sensorless technique is evaluated using simulations and real-world testing utilising a three-phase test SRM. Novel methods have been developed to remove the effects of mutual coupling, flux-linkage term errors and magnetic hysteresis. These techniques ensure that accurate sensorless operation is achievable over a wide range of speeds and loads, without the need for an offline calibration technique. This results in improved durability and reduced cost of the SRM.

In recent years, the capabilities of legged locomotion controllers have been significantly advanced enabling them to traverse basic types of uneven terrain without visual perception. However, safely and autonomously traversing longer distances over difficult uneven terrain requires appropriate motion planning using online collected environmental knowledge. In this paper, we present such a novel methodology for generic closed-loop preceding horizon footstep planning that enables legged robots equipped with capable locomotion controllers to autonomously traverse previously unknown terrain while continuously walking long distances. Hereby, our approach addresses the challenge of online terrain perception and soft real-time footstep planning. The proposed new formulation of the search-based planning problem makes no specific assumptions about the robot kinematics (e.g. number of legs) or the used locomotion control schemes. Therefore, it can be applied to a broad range of different types of legged robots. Unlike current methods, the proposed new framework can optionally consider the floating base as part of the state-space. It is possible to configure the complexity of the planner online, from efficiently solving tasks in flat terrain to using non-contiguous contacts in highly challenging terrain. Finally, the presented methodology is successfully applied and evaluated in virtual and real experiments on state of the art bipedal, quadrupedal, and a novel eight-legged robot.