With the widespread use of doubly-fed induction generators (DFIG) in wind energy conversion systems, condition monitoring is being given importance. Non-intrusive techniques like motor current signature analysis (MCSA), which involves looking for specific frequency components in the current spectrum, are preferred over analysis of magnetic field, temperature, vibrations or acoustic noise which require additional sensors. The major difficulty in MCSA is isolation of the fault, as multiple faults produce similar signatures. Moreover, closed-loop control makes diagnostics more complicated due to inherent compensation by the controller. This thesis presents a method to diagnose static and dynamic air gap eccentricities in doubly-fed induction generators operated for closed-loop stator power control by using a modified control technique to enable detection and isolation of this fault from electrical unbalances in the stator and rotor and load torque oscillations that produce similar effects. The effectiveness of the proposed control is verified using simulations and preliminary experiments performed on a healthy machine.

The restoration of arm-free standing in paraplegic individuals can be accomplished with the help of functional electrical stimulation (FES). The key component of such a system is a controller that can modulate FES induced muscle contractions in real-time, such that artificially produced forces in the legs and abdominal muscles are able to generate stable standing posture. A 57 year-old individual with chronic ASIA-A (American Spinal Injury Association), T3/4 level spinal cord injury (SCI) participated in this study. The objective was to determine if a proportional-derivative (PD) or proportional-integral-derivative (PID) controller could be used to regulate FES induced muscle contractions in the ankle joint to allow it to maintain balance of the entire body during quiet standing, while exhibiting physiological dynamics seen in able-bodied individuals while doing so.

<p>Deep brain stimulation (DBS) is an established surgical therapy for movement disorders. The mechanisms of action of DBS remain unclear, and selection of stimulation parameters is a clinical challenge and can result in sub-optimal outcomes. Closed-loop DBS systems would use a feedback control signal for automatic adjustment of DBS parameters and improved therapeutic effectiveness. We hypothesized that evoked compound action potentials (ECAPs), generated by activated neurons in the vicinity of the stimulating electrode, would reveal the type and spatial extent of neural activation, as well as provide signatures of clinical effectiveness. The objective of this dissertation was to record and characterize the ECAP during DBS to determine its suitability as a feedback signal in closed-loop systems. The ECAP was investigated using computer simulation and <italic>in vivo</italic> experiments, including the first preclinical and clinical ECAP recordings made from the same DBS electrode implanted for stimulation. </p><p>First, we developed DBS-ECAP recording instrumentation to reduce the stimulus artifact and enable high fidelity measurements of the ECAP at short latency. <italic>In vitro</italic> and <italic>in vivo</italic> validation experiments demonstrated the capability of the instrumentation to suppress the stimulus artifact, increase amplifier gain, and reduce distortion of short latency ECAP signals.</p><p>Second, we characterized ECAPs measured during thalamic DBS across stimulation parameters in anesthetized cats, and determined the neural origin of the ECAP using pharmacological interventions and a computer-based biophysical model of a thalamic network. This model simulated the ECAP response generated by a population of thalamic neurons, calculated ECAPs similar to experimental recordings, and indicated the relative contribution from different types of neural elements to the composite ECAP. Signal energy of the ECAP increased with DBS amplitude or pulse width, reflecting an increased extent of activation. Shorter latency, primary ECAP phases were generated by direct excitation of neural elements, whereas longer latency, secondary phases were generated by post-synaptic activation.</p><p>Third, intraoperative studies were conducted in human subjects with thalamic DBS for tremor, and the ECAP and tremor responses were measured across stimulation parameters. ECAP recording was technically challenging due to the presence of a wide range of stimulus artifact magnitudes across subjects, and an electrical circuit equivalent model and finite element method model both suggested that glial encapsulation around the DBS electrode increased the artifact size. Nevertheless, high fidelity ECAPs were recorded from acutely and chronically implanted DBS electrodes, and the energy of ECAP phases was correlated with changes in tremor. </p><p>Fourth, we used a computational model to understand how electrode design parameters influenced neural recording. Reducing the diameter or length of recording contacts increased the magnitude of single-unit responses, led to greater spatial sensitivity, and changed the relative contribution from local cells or passing axons. The effect of diameter or contact length varied across phases of population ECAPs, but ECAP signal energy increased with greater contact spacing, due to changes in the spatial sensitivity of the contacts. In addition, the signal increased with glial encapsulation in the peri-electrode space, decreased with local edema, and was unaffected by the physical presence of the highly conductive recording contacts.</p><p>It is feasible to record ECAP signals during DBS, and the correlation between ECAP characteristics and tremor suggests that this signal could be used in closed-loop DBS. This was demonstrated by implementation in simulation of a closed-loop system, in which a proportional-integral-derivative (PID) controller automatically adjusted DBS parameters to obtain a target ECAP energy value, and modified parameters in response to disturbances. The ECAP also provided insight into neural activation during DBS, with the dominant contribution to clinical ECAPs derived from excited cerebellothalamic fibers, suggesting that activation of these fibers is critical for DBS therapy.</p> / Dissertation

The restoration of arm-free standing in paraplegic individuals can be accomplished with the help of functional electrical stimulation (FES). The key component of such a system is a controller that can modulate FES induced muscle contractions in real-time, such that artificially produced forces in the legs and abdominal muscles are able to generate stable standing posture. A 57 year-old individual with chronic ASIA-A (American Spinal Injury Association), T3/4 level spinal cord injury (SCI) participated in this study. The objective was to determine if a proportional-derivative (PD) or proportional-integral-derivative (PID) controller could be used to regulate FES induced muscle contractions in the ankle joint to allow it to maintain balance of the entire body during quiet standing, while exhibiting physiological dynamics seen in able-bodied individuals while doing so.

abstract: Advances in implantable MEMS technology has made possible adaptive micro-robotic implants that can track and record from single neurons in the brain. Development of autonomous neural interfaces opens up exciting possibilities of micro-robots performing standard electrophysiological techniques that would previously take researchers several hundred hours to train and achieve the desired skill level. It would result in more reliable and adaptive neural interfaces that could record optimal neural activity 24/7 with high fidelity signals, high yield and increased throughput. The main contribution here is validating adaptive strategies to overcome challenges in autonomous navigation of microelectrodes inside the brain. The following issues pose significant challenges as brain tissue is both functionally and structurally dynamic: a) time varying mechanical properties of the brain tissue-microelectrode interface due to the hyperelastic, viscoelastic nature of brain tissue b) non-stationarities in the neural signal caused by mechanical and physiological events in the interface and c) the lack of visual feedback of microelectrode position in brain tissue. A closed loop control algorithm is proposed here for autonomous navigation of microelectrodes in brain tissue while optimizing the signal-to-noise ratio of multi-unit neural recordings. The algorithm incorporates a quantitative understanding of constitutive mechanical properties of soft viscoelastic tissue like the brain and is guided by models that predict stresses developed in brain tissue during movement of the microelectrode. An optimal movement strategy is developed that achieves precise positioning of microelectrodes in the brain by minimizing the stresses developed in the surrounding tissue during navigation and maximizing the speed of movement. Results of testing the closed-loop control paradigm in short-term rodent experiments validated that it was possible to achieve a consistently high quality SNR throughout the duration of the experiment. At the systems level, new generation of MEMS actuators for movable microelectrode array are characterized and the MEMS device operation parameters are optimized for improved performance and reliability. Further, recommendations for packaging to minimize the form factor of the implant; design of device mounting and implantation techniques of MEMS microelectrode array to enhance the longevity of the implant are also included in a top-down approach to achieve a reliable brain interface. / Dissertation/Thesis / Ph.D. Bioengineering 2013

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