High-accuracy Motion Estimation for MEMS Devices with Capacitive Sensors

With the development of micro-electro-mechanical system (MEMS) technologies, emerging MEMS applications such as in-situ MEMS IMU calibration, medical imaging via endomicroscopy, and feedback control for nano-positioning and laser scanning impose needs for especially accurate measurements of motion using on-chip sensors. Due to their advantages of simple fabrication and integration within system level architectures, capacitive sensors are a primary choice for motion tracking in those applications. However, challenges arise as often the capacitive sensing scheme in those applications is unconventional due to the nature of the application and/or the design and fabrication restrictions imposed, and MEMS sensors are traditionally susceptible to accuracy errors, as from nonlinear sensor behavior, gain and bias drift, feedthrough disturbances, etc. Those challenges prevent traditional sensing and estimation techniques from fulfilling the accuracy requirements of the candidate applications. The goal of this dissertation is to provide a framework for such MEMS devices to achieve high-accuracy motion estimation, and specifically to focus on innovative sensing and estimation techniques that leverage unconventional capacitive sensing schemes to improve estimation accuracy. Several research studies with this specific aim have been conducted, and the methodologies, results and findings are presented in the context of three applications. The general procedure of the study includes proposing and devising the capacitive sensing scheme, deriving a sensor model based on first principles of capacitor configuration and sensing circuit, analyzing the sensor’s characteristics in simulation with tuning of key parameters, conducting experimental investigations by constructing testbeds and identifying actuation and sensing models, formulating estimation schemes is to include identified actuation dynamics and sensor models, and validating the estimation schemes and evaluating their performance against ground truth measurements. The studies show that the proposed techniques are valid and effective, as the estimation schemes adopted either fulfill the requirements imposed or improve the overall estimation performance. Highlighted results presented in this dissertation include a scale factor calibration accuracy of 286 ppm for a MEMS gyroscope (Chapter 3), an improvement of 15.1% of angular displacement estimation accuracy by adopting a threshold sensing technique for a scanning micro-mirror (Chapter 4), and a phase shift prediction error of 0.39 degree for a electrostatic micro-scanner using shared electrodes for actuation and sensing (Chapter 5).PHDMechanical EngineeringUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttps://deepblue.lib.umich.edu/bitstream/2027.42/147568/1/davidsky_1.pd

Tracking bridge tilt behaviour using sensor fusion techniques

This is the final version. Available on open access from Springer via the DOI in this recordThe resilience of the built environment to extreme weather events is fundamental for the day-to-day operation of our transport network, with scour representing one of the biggest threats to bridges built over flowing water. Condition monitoring of the bridge using a structural health monitoring system enhances resilience by reducing the time needed to return the bridge to normal use by providing timely information on structural condition and safety. The work presented in this report discusses use of rotational measurements in structural health monitoring. Traditionally tiltmeters (which can be a form of DC accelerometer) are used to measure rotation but are known to be affected by dynamic movements, while gyroscopes react quickly to dynamic motion but drift over time. This review will introduce gyroscopes as a complementary sensor for accelerometer rotational measurements and use sensor fusion techniques to combine the measurements from both sensors to get an optimised rotational result. This method was trialled on a laboratory scaled model, before the system was installed on an in-service single-span skewed railway bridge. The rotational measurements were compared against rotation measurements obtained using a vision-based measurement system to confirm the validity of the results. An introduction to gyroscopes, field test measurement results with the sensors and their correlation with the vision-based measurement results are presented in this article.Engineering and Physical Sciences Research Council (EPSRC

Integrated control and health management. Orbit transfer rocket engine technology program

To insure controllability of the baseline design for a 7500 pound thrust, 10:1 throttleable, dual expanded cycle, Hydrogen-Oxygen, orbit transfer rocket engine, an Integrated Controls and Health Monitoring concept was developed. This included: (1) Dynamic engine simulations using a TUTSIM derived computer code; (2) analysis of various control methods; (3) Failure Modes Analysis to identify critical sensors; (4) Survey of applicable sensors technology; and, (5) Study of Health Monitoring philosophies. The engine design was found to be controllable over the full throttling range by using 13 valves, including an oxygen turbine bypass valve to control mixture ratio, and a hydrogen turbine bypass valve, used in conjunction with the oxygen bypass to control thrust. Classic feedback control methods are proposed along with specific requirements for valves, sensors, and the controller. Expanding on the control system, a Health Monitoring system is proposed including suggested computing methods and the following recommended sensors: (1) Fiber optic and silicon bearing deflectometers; (2) Capacitive shaft displacement sensors; and (3) Hot spot thermocouple arrays. Further work is needed to refine and verify the dynamic simulations and control algorithms, to advance sensor capabilities, and to develop the Health Monitoring computational methods

An Alternative Sensor Fusion Method For Object Orientation Using Low-Cost Mems Inertial Sensors

This thesis develops an alternative sensor fusion approach for object orientation using low-cost MEMS inertial sensors. The alternative approach focuses on the unique challenges of small UAVs. Such challenges include the vibrational induced noise onto the accelerometer and bias offset errors of the rate gyroscope. To overcome these challenges, a sensor fusion algorithm combines the measured data from the accelerometer and rate gyroscope to achieve a single output free from vibrational noise and bias offset errors. One of the most prevalent sensor fusion algorithms used for orientation estimation is the Extended Kalman filter (EKF). The EKF filter performs the fusion process by first creating the process model using the nonlinear equations of motion and then establishing a measurement model. With the process and measurement models established, the filter operates by propagating the mean and covariance of the states through time. The success of EKF relies on the ability to establish a representative process and measurement model of the system. In most applications, the EKF measurement model utilizes the accelerometer and GPS-derived accelerations to determine an estimate of the orientation. However, if the GPS-derived accelerations are not available then the measurement model becomes less reliable when subjected to harsh vibrational environments. This situation led to the alternative approach, which focuses on the correlation between the rate gyroscope and accelerometer-derived angle. The correlation between the two sensors then determines how much the algorithm will use one sensor over the other. The result is a measurement that does not suffer from the vibrational noise or from bias offset errors