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This thesis proposes an outline for a framework for an evaluation method that takes as an input a model of an artificial hand, which claims to be anthropomorphic, and produces as output the set of tasks that the hand can perform. The framework is based on studying the literature on the anatomy and functionalities of the human hand and methods of implementing these functionalities in artificial systems. The thesis also presents a partial implementation of the framework which focuses on tasks of gesturing and grasping using anthropomorphic postures. This thesis focuses on the evaluation of the intrinsic hardware of robot hands from technical and functional perspectives, including kinematics of the mechanical structure, geometry of the contact surface, and functional force conditions for successful grasps. This thesis does not consider topics related to control or elements of aesthetics of the design of robot hands. The thesis reviews the literature on the anatomy, motion and sensory capabilities, and functionalities of the human hand to define a reference to evaluate artificial hands. It distinguishes between the hands construction and functionalities and presents a discussion of anthropomorphism that reflects this distinction. It reviews key theory related to artificial hands and notable solutions and existing methods of evaluating artificial hands. The thesis outlines the evaluation framework by defining the action manifold of the anthropomorphic hand, defined as the set of all tasks that a hypothetical ideal anthropomorphic hand should be able to do, and analysing the manifold tasks to determine the hand capabilities involved in the tasks and how to simulate them. A syntax is defined to describe hand tasks and anthropomorphic postures. The action manifold is defined to be used as a functional reference to evaluate artificial hands’ performance. A method to evaluate anthropomorphic postures using Fuzzy logic and a method to evaluate anthropomorphic grasping abilities are proposed and applied on models of the human hand and the InMoov robot hand. The results show the methods’ ability to detect successful postures and grasps. Future work towards a full implementation of the framework is suggested.

The research presented in this thesis investigates the viability of utilising a mobile robot as a substitute for faulty static sensors exhibiting anomalous behaviour at a door in a Smart Environment. This is implemented through the development of finite state automata for the management of static sensor events. Novel approaches for the introduction of adaptability to finite state automata and the investigation of static sensor anomalies are proposed. This research was executed through the use of both practical and theoretical investigations. The ideas proposed in this work were developed as software artefacts. The functionality of that software was tested in order to assess the validity of the approaches proposed within this body of work. The results of this research show that the utilisation of a mobile robot in a Smart Environment is a viable means of providing substitution for static sensor functionality about a door and can be used as a means of verifying the presence of a systemic static sensor anomaly. Adaptive finite state automata were successfully developed and validated as a means of implementing the self-healing property of the Autonomic Computing paradigm in a software system. The limitations of this research have been identified and considered in the conclusion of this thesis as areas for future work.

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This thesis is concerned with the analysis of the multimodal behaviour and transition characteristics of the jump phenomenon in nonlinear coupled circuits. The work is based on earlier work done in studying second order nonlinear systems whereby a graphical method based on the describing function and integral curve representation is employed to estimate transients. The analysis of a simple series nonlinear L.C.R. cirucit is carried further to include a shorted secondary coil. A further step in the analysis is reached when a voltage in series with the shorted secondary is considered and. the analysis carried out to determine the effect of this secondary voltage on the behaviour of the system. This is done for different phases and amplitudes of the secondary voltage when applied to the system at different instants of time. These two cases are fully analysed both in the steady state and transiently. The overall transformer coupled circuit is then considered and due to the complexity of the circuit the analysis is done by assuming that the transformer coupled circuit be replaced by an equivalent T-network of inductances. This representation results in three second order nonlinear differential equations for the system which can be solved for the steady state. With some approximations a transient transpose analysis even is possible and the simulation of the system gives more insight into its transitional behaviour. In all these systems considerable use is made of the digital computer to plot the integral curves and the transients derived from them. The analogue computer is also extensively used to verify the theoretical results. Methods whereby integral curves can be related to the time varying quantities in the systems are given and it is shown how initial conditions leading to a given mode of response can be estimated. The whole work in this thesis is directed mostly to the question of the transition of the current (or flux) by means of a mutually coupled circuit so that it can be applied to the development of an amplitude oontrol scheme for the application of osoillating motors to bobbin winders.

Receding horizon optimal control has a long history and provides the basis for several of the currently popular techniques for model predictive control of chemical processes. Based on a prediction of the current state of the process, derived from plant measurements, and predictions of the likely input conditions over a certain period ahead (the time horizon), optimal controls are computed to maximise a given "performance function", which can be either an economic or technical measure of performance. The state predictions are regularly updated as new measurements are made and the controls are re-computed if either the current state or the predicted input conditions change significantly. An important advantage of computing optimal controls is that this can take direct account of process operating constraints. However, general inequality constraints have always posed a difficult problem in numerical optimal control algorithms and we propose a new algorithm for dealing with this problem for systems described by DAEs of any index. Due to both the excessive time required for model development and the excessive computational time required for a full model, the on-line model is generally of a much lower complexity than the true system and although parameters in the model may be re-estimated using plant measurements, there is inevitably some uncertainty in the model predictions as well as in the input predictions. The controls must therefore be robust, giving reasonable performance in the face of variations of actual conditions from the predictions. To obtain a sufficiently simple controller, the H∞ approach has been to design a controller to perform optimally if the worst possible set of inputs and model predictions actually occur, and this can also be applied within the receding horizon optimal control framework. The result is a game, with the controls playing against the uncertain inputs and model parameters. However, this does not guarantee robustness in the presence of inequality constraints and here we propose a new simple technique for dealing with this problem, which exploits the sampling interval of the receding horizon formulation of the problem.

Micro robotics has the potential to improve the efficiency and reduce cost of future multi-object instruments for astronomy. This thesis reports on the development and evolution of a micro autonomous pick-off mirror called the Micro Autonomous Positioning System (MAPS) that can be used in a multi-object spectrograph. The design of these micro-autonomous pick-off mirrors is novel as they are capable of high precision positioning using electromagnetic propulsion through utilising non-conventional components and techniques. These devices are self-driven robotic units, which with the help of an external control system are capable of positioning themselves on an instruments focal plane to within 24 μm. This is different from other high precision micro robotics as they normally use piezoelectric actuators for propulsion. Micro robots have been developed that use electromagnetic motors, however they are not used for high precision applications. Although there is a plethora of literature covering design, functionality and capability of precision micro autonomous systems, there is limited research on characterisation methods for their use in astronomical applications. This work contributes not only to the science supporting the design of a micro-autonomous pick-off mirror but also presents a framework for characterising such miniature mechanisms. The majority of instruments are presented with a curved focal plane. Therefore, to ensure that the pick-off mirrors are aligned properly with the receiving optics, either the pick-off mirror needs to be tipped or the receiving optics repositioned. Currently this function is implemented in the beam steering mirror (i.e. the receiving optics). The travel range required by the beam steering mirror is relatively large, and as such, it is more difficult to achieve the positional accuracy and stability. By incorporating this functionality in the pick-off mirror, the instrument can be optimised in terms of size, accuracy and stability. A unique self-adjusting mirror (SAM) is thus proposed as a solution and detailed. As a proof-of-concepts both MAPS and SAM usability in multi-object spectrographs was evaluated and validated. The results indicate their potential to meet the requirements of astronomical instruments and reduce both the size and cost.

Recent research on mobile robotics has produced new designs that provide house-hold robots with omnidirectional motion. The image sensor embedded in these devices motivates the application of 3D vision techniques on them for navigation and mapping purposes. In addition to this, distributed cheapsensing systems acting as unitary entity have recently been discovered as an efficient alternative to expensive mobile equipment. In this work we present an implementation of a visual reconstruction method, structure from motion (SfM), on a low-budget, omnidirectional mobile platform, and extend this method to distributed 3D scene reconstruction with several instances of such a platform. Our approach overcomes the challenges yielded by the plaform. The unprecedented levels of noise produced by the image compression typical of the platform is processed by our feature filtering methods, which ensure suitable feature matching populations for epipolar geometry estimation by means of a strict quality-based feature selection. The robust pose estimation algorithms implemented, along with a novel feature tracking system, enable our incremental SfM approach to novelly deal with ill-conditioned inter-image configurations provoked by the omnidirectional motion. The feature tracking system developed efficiently manages the feature scarcity produced by noise and outputs quality feature tracks, which allow robust 3D mapping of a given scene even if - due to noise - their length is shorter than what it is usually assumed for performing stable 3D reconstructions. The distributed reconstruction from multiple instances of SfM is attained by applying loop-closing techniques. Our multiple reconstruction system merges individual 3D structures and resolves the global scale problem with minimal overlaps, whereas in the literature 3D mapping is obtained by overlapping stretches of sequences. The performance of this system is demonstrated in the 2-session case. The management of noise, the stability against ill-configurations and the robustness of our SfM system is validated on a number of experiments and compared with state-of-the-art approaches. Possible future research areas are also discussed.

Reliable process data are the basis for efficient process evaluation, operation and control. When flawed information is used for process monitoring and control, the state of the system is misrepresented and the performances of the control and fault detection schemes may be poor. Therefore, to improve the estimation accuracy and reduce measurement uncertainties is essential for the optimal operation of intelligent monitoring, control and fault detection schemes in all kinds of systems. The work described in this thesis is aimed at tackling a particular kind of measurement error: systematic error (or sensor bias) that cannot be removed by calibration or simply installing a more accurate sensor. This kind of systematic error can occur when a sensor is not directly measuring the quantity that needs to be measured, for example, when the measured quantity varies spatially or when the sensor is not positioned at an appropriate location. The method adopted in this thesis uses Computational Fluid Dynamics (CFD) simulation to estimate the systematic errors caused by spatial variations and data fusion to combine the direct measurements with other knowledge to reduce measurement uncertainties which include both random errors and systematic errors. The application of this method in airconditioning systems has been described. The control signals for the actuators of the fans and the mixing box dampers, which are available through the building energy management system, can be used to estimate the airflow rates. CFD simulations are carried out to examine the relationship between the values of the systematic errors on the measurements of the mixed-air flow rate and temperature and the operation conditions. Results obtained from the simulation of a symmetric mixing box are used to derive mathematical models that can be used to estimate the measurement biases. The uncertainties associated with the CFD simulations and the bias estimators are evaluated. A method of fuzzy data fusion, which incorporates a compatibility function, is proposed. Both a statistical data fusion scheme and a fuzzy data fusion scheme are used to reduce the estimation errors associated with the supply, return and inlet airflow rates and the mixed-air temperature in air-conditioning systems. The estimates obtained from the data fusion schemes are used in a fuzzy fault detection scheme for the cooling coil. Promising results are obtained. It is shown that the use of the data fusion schemes, together with the estimation of the systematic errors, can greatly reduce the overall measurement uncertainties and improve the performance of the fault diagnosis scheme when the estimates the systematic errors are unbiased. It is concluded that fuzzy data fusion should be used if the systematic errors are varying and the estimates of the systematic errors are themselves biased.