Cascoded GaN half-bridge with 17-MHz wide-band galvanically isolated current sensor

A cascoded GaN half-bridge with wide-band galvanically isolated current sensor is proposed. A 650-V depletion-mode GaN FET is switched by a low-propagation-delay gate driver in active-mode. The standby and active modes are switched by a 25-V N-ch LDMOS. The current sensor uses the LDMOS as a shunt resistor, gm-cell-based sense amplifier and mixer based isolation amplifier for wider bandwidth. PVT variations of on-resistance of the current-detecting MOSFET are compensated using a reference MOSFET. A digital calibration loop across the isolation is formed to keep the current sensor gain constant within ±1.5% across the whole temperature range. The wide-band current sensor can measure power device switching current. In this study, a cascoded GaN half-bridge switching and inductor current sensing using low-side and high-side device current are demonstrated. The proposed techniques show the possibility of implementing a GaN half-bridge module with isolated current sensor in a package.

Mechatronic system parameter idenfitcation [i.e. identification] via genetic algorithms

This thesis develops a novel way to identify both the joint friction parameters and a built in torque sensor gain and offset. The identification method is based on a genetic algorithm (GA). A model based friction compensation method and a real coded GA are selected from a variety of methods available. A model of a single degree of freedom mechatronic joint with a link is presented. Numerical simulations are run to determine the optimum configuration of the GA with respect to the population size and maximum number of generations necessary to identify the parameters to within 5% of their actual value. The GA identification technique is then used on an experimental mechatronic joint with a harmonic drive and built-in torque sensor. The friction parameters as well as the sensor gain and offset are identified in the experimental system and the position tracking error is reduced. Based on the experimental results, the method is found to be an effective way of identifying system parameters in a mechatronic joint.

Mechatronic system parameter idenfitcation [i.e. identification] via genetic algorithms

This thesis develops a novel way to identify both the joint friction parameters and a built in torque sensor gain and offset. The identification method is based on a genetic algorithm (GA). A model based friction compensation method and a real coded GA are selected from a variety of methods available. A model of a single degree of freedom mechatronic joint with a link is presented. Numerical simulations are run to determine the optimum configuration of the GA with respect to the population size and maximum number of generations necessary to identify the parameters to within 5% of their actual value. The GA identification technique is then used on an experimental mechatronic joint with a harmonic drive and built-in torque sensor. The friction parameters as well as the sensor gain and offset are identified in the experimental system and the position tracking error is reduced. Based on the experimental results, the method is found to be an effective way of identifying system parameters in a mechatronic joint.

Pragmatic spatial sampling for wearable MEG arrays

Several new technologies have emerged promising new Magnetoencephalography (MEG) systems in which the sensors can be placed close to the scalp. One such technology, Optically Pumped MEG (OP-MEG) allows for a scalp mounted system that provides measurements within millimetres of the scalp surface. A question that arises in developing on-scalp systems is: how many sensors are necessary to achieve adequate performance/spatial discrimination? There are many factors to consider in answering this question such as the signal to noise ratio (SNR), the locations and depths of the sources, density of spatial sampling, sensor gain errors (due to interference, subject movement, cross-talk, etc.) and, of course, the desired spatial discrimination. In this paper, we provide simulations which show the impact these factors have on designing sensor arrays for wearable MEG. While OP-MEG has the potential to provide high information content at dense spatial samplings, we find that adequate spatial discrimination of sources (< 1 cm) can be achieved with relatively few sensors (< 100) at coarse spatial samplings (~ 30 mm) at high SNR. After this point approximately 50 more sensors are required for every 1 mm improvement in spatial discrimination. Comparable discrimination for traditional cryogenic systems require more channels by these same metrics. We also show that sensor gain errors have the greatest impact on discrimination between deep sources at high SNR. Finally, we also examine the limitation that aliasing due to undersampling has on the effective SNR of on-scalp sensors.