Fixed-wing MAV attitude stability in atmospheric turbulence, part 1: Suitability of conventional sensors

Fixed-wing Micro-Aerial Vehicles (MAVs) need effective sensors that can rapidly detect turbulence induced motion perturbations. Current MAV attitude control systems rely on inertial sensors. These systems can be described as reactive; detecting the disturbance only after the aircraft has responded to the disturbing phenomena. In this part of the paper, the current state of the art in reactive attitude sensing for fixed-wing MAVs are reviewed. A scheme for classifying the range of existing and emerging sensing techniques is presented. The features and performance of the sensing approaches are discussed in the context of their application to MAV attitude control systems in turbulent environments. It is found that the use of single sensors is insufficient for MAV control in the presence of turbulence and that potential gains can be realised from multi-sensor systems. A successive paper to be published in this journal will investigate novel attitude sensors which have the potential to improve attitude control of MAVs in Turbulenc

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

Stabilization of a thermal camera at sea

During, for example, search-and-rescue operations at sea, technical equipment like spotlights and thermal cameras are important aids. However, heave and sway affect the ship and make it harder for finding a person in distress. This thesis presents a way of stabilizing a thermal camera by controlling a stepper motor connected to it. Moreover, the thermal camera can only be turned upwards and downwards. The whole process was investigated to see what can and needs to be measured for stabilization at sea to work. With this knowledge, sensors had to be chosen to collect the necessary measurements. The measurements from the different sensors were merged together using a Kalman filter to give an estimation for the tilt and elevation of a ship, which was used for controlling the stepper motor. Moreover, the control of the stepper motor was done using PID control. The process was simulated in Simulink, making it possible to tune different parameters so that good performance was ensured based on assumed models. This was then implemented on the real system and tests were carried out to verify what was found during simulations. The result from this thesis is a stabilized thermal camera which helps an operator enormously in searching and finding objects at sea

Master of Science

thesisComputing and data acquisition have become an integral part of everyday life. From reading emails on cell phones to kids playing with motion sensing game consoles, we are surrounded with sensors and mobile computing devices. As the availability of powerful computing devices increases, applications in previously limited environments become possible. Training devices in rehabilitation are becoming increasingly common and more mobile. Community based rehabilitative devices are emerging that embrace these mobile advances. To further the flexibility of devices used in rehabilitation, research has explored the use of smartphones as a means to process data and provide feedback to the user. In combination with sensor embedded insoles, smartphones provide a powerful tool for the clinician in gathering data and as a standalone training tool in rehabilitation. This thesis presents the continuing research of sensor based insoles, feedback systems and increasing the capabilities of the Adaptive Real-Time Instrumentation System for Tread Imbalance Correction, or ARTISTIC, with the introduction of ARTISTIC 2.0. To increase the capabilities of the ARTISTIC an Inertial Measurement Unit (IMU) was added, which gave the system the ability to quantify the motion of the gait cycle and, more specifically, determine stride length. The number of sensors in the insole was increased from two to ten, as well as placing the microprocessor and a vibratory motor in the insole. The transmission box weight was reduced by over 50 percent and the volume by over 60 percent. Stride length was validated against a motion capture system and found the average stride length to be within 2.7 ± 6.9 percent. To continue the improvement of the ARTISTIC 2.0, future work will include implementing real-time stride length feedback

A Kalman Filter Based Attitude Heading Reference System Using a Low Cost Inertial Measurement Unit

This paper describes, the development of a sensor fusion algorithm-based Kalman lter ar- chitecture, in combination with a low cost Inertial Measurement Unit (IMU) for an Attitude Heading Reference System (AHRS). A low cost IMU takes advantage of the use of MEMS technology enabling cheap, compact, low grade sensors. The use of low cost IMUs is primar- ily targeted towards Unmanned Aerial Vehicle (UAV) applications due to the requirements for small package size, light weight, and low energy consumption. The high dynamics nature of smaller airframes, coupled with the typical vibration induced noise of UAVs require an e cient, reliable, and robust AHRS for vehicle control. To eliminate the singularities at 90 on the pitch and roll axes, and to keep the computational e ciency high, quaternions are used for state attitude representation

Generic Multisensor Integration Strategy and Innovative Error Analysis for Integrated Navigation

A modern multisensor integrated navigation system applied in most of civilian applications typically consists of GNSS (Global Navigation Satellite System) receivers, IMUs (Inertial Measurement Unit), and/or other sensors, e.g., odometers and cameras. With the increasing availabilities of low-cost sensors, more research and development activities aim to build a cost-effective system without sacrificing navigational performance. Three principal contributions of this dissertation are as follows: i) A multisensor kinematic positioning and navigation system built on Linux Operating System (OS) with Real Time Application Interface (RTAI), York University Multisensor Integrated System (YUMIS), was designed and realized to integrate GNSS receivers, IMUs, and cameras. YUMIS sets a good example of a low-cost yet high-performance multisensor inertial navigation system and lays the ground work in a practical and economic way for the personnel training in following academic researches. ii) A generic multisensor integration strategy (GMIS) was proposed, which features a) the core system model is developed upon the kinematics of a rigid body; b) all sensor measurements are taken as raw measurement in Kalman filter without differentiation. The essential competitive advantages of GMIS over the conventional error-state based strategies are: 1) the influences of the IMU measurement noises on the final navigation solutions are effectively mitigated because of the increased measurement redundancy upon the angular rate and acceleration of a rigid body; 2) The state and measurement vectors in the estimator with GMIS can be easily expanded to fuse multiple inertial sensors and all other types of measurements, e.g., delta positions; 3) one can directly perform error analysis upon both raw sensor data (measurement noise analysis) and virtual zero-mean process noise measurements (process noise analysis) through the corresponding measurement residuals of the individual measurements and the process noise measurements. iii) The a posteriori variance component estimation (VCE) was innovatively accomplished as an advanced analytical tool in the extended Kalman Filter employed by the GMIS, which makes possible the error analysis of the raw IMU measurements for the very first time, together with the individual independent components in the process noise vector

Design and analysis of a high-gain and robust multi-DOF electro-thermally actuated MEMS gyroscope

© 2018 by the authors. This paper presents the design and analysis of a multi degree of freedom (DOF) electro-thermally actuated non-resonant MEMS gyroscope with a 3-DOF drive mode and 1-DOF sense mode system. The 3-DOF drive mode system consists of three masses coupled together using suspension beams. The 1-DOF system consists of a single mass whose motion is decoupled from the drive mode using a decoupling frame. The gyroscope is designed to be operated in the flat region between the first two resonant peaks in drive mode, thus minimizing the effect of environmental and fabrication process variations on device performance. The high gain in the flat operational region is achieved by tuning the suspension beams stiffness. A detailed analytical model, considering the dynamics of both the electro-thermal actuator and multi-mass system, is developed. A parametric optimization is carried out, considering the microfabrication process constraints of the Metal Multi-User MEMS Processes (MetalMUMPs), to achieve high gain. The stiffness of suspension beams is optimized such that the sense mode resonant frequency lies in the flat region between the first two resonant peaks in the drive mode. The results acquired through the developed analytical model are verified with the help of 3D finite element method (FEM)-based simulations. The first three resonant frequencies in the drive mode are designed to be 2.51 kHz, 3.68 kHz, and 5.77 kHz, respectively. The sense mode resonant frequency is designed to be 3.13 kHz. At an actuation voltage of 0.2 V, the dynamically amplified drive mode gain in the sense mass is obtained to be 18.6 μm. With this gain, a capacitive change of 28.11 f F and 862.13 f F is achieved corresponding to the sense mode amplitude of 0.15 μm and 4.5 μm at atmospheric air pressure and in a vacuum, respectively

MEMS Accelerometers

Micro-electro-mechanical system (MEMS) devices are widely used for inertia, pressure, and ultrasound sensing applications. Research on integrated MEMS technology has undergone extensive development driven by the requirements of a compact footprint, low cost, and increased functionality. Accelerometers are among the most widely used sensors implemented in MEMS technology. MEMS accelerometers are showing a growing presence in almost all industries ranging from automotive to medical. A traditional MEMS accelerometer employs a proof mass suspended to springs, which displaces in response to an external acceleration. A single proof mass can be used for one- or multi-axis sensing. A variety of transduction mechanisms have been used to detect the displacement. They include capacitive, piezoelectric, thermal, tunneling, and optical mechanisms. Capacitive accelerometers are widely used due to their DC measurement interface, thermal stability, reliability, and low cost. However, they are sensitive to electromagnetic field interferences and have poor performance for high-end applications (e.g., precise attitude control for the satellite). Over the past three decades, steady progress has been made in the area of optical accelerometers for high-performance and high-sensitivity applications but several challenges are still to be tackled by researchers and engineers to fully realize opto-mechanical accelerometers, such as chip-scale integration, scaling, low bandwidth, etc

Virtual Model Building for Multi-Axis Machine Tools Using Field Data

Accurate machine dynamic models are the foundation of many advanced machining technologies such as virtual process planning and machine condition monitoring. Viewing recent designs of modern high-performance machine tools, to enhance the machine versatility and productivity, the machine axis configuration is becoming more complex and diversified, and direct drive motors are more commonly used. Due to the above trends, coupled and nonlinear multibody dynamics in machine tools are gaining more attention. Also, vibration due to limited structural rigidity is an important issue that must be considered simultaneously. Hence, this research aims at building high-fidelity machine dynamic models that are capable of predicting the dynamic responses, such as the tracking error and motor current signals, considering a wide range of dynamic effects such as structural flexibility, inter-axis coupling, and posture-dependency. Building machine dynamic models via conventional bottom-up approaches may require extensive investigation on every single component. Such approaches are time-consuming or sometimes infeasible for the machine end-users. Alternatively, as the recent trend of Industry 4.0, utilizing data via Computer Numerical Controls (CNCs) and/or non-intrusive sensors to build the machine model is rather favorable for industrial implementation. Thus, the methods proposed in this thesis are top-down model building approaches, utilizing available data from CNCs and/or other auxiliary sensors. The achieved contributions and results of this thesis are summarized below. As the first contribution, a new modeling and identification technique targeting a closed-loop control system of coupled rigid multi-axis feed drives has been developed. A multi-axis closed-loop control system, including the controller and the electromechanical plant, is described by a multiple-input multiple-output (MIMO) linear time-invariant (LTI) system, coupled with a generalized disturbance input that represents all the nonlinear dynamics. Then, the parameters of the open-loop and closed-loop dynamic models are respectively identified by a strategy that combines linear Least Squares (LS) and constrained global optimization. This strategy strikes a balance between model accuracy and computational efficiency. This proposed method was validated using an industrial 5-axis laser drilling machine and an experimental feed drive, achieving 2.38% and 5.26% root mean square (RMS) prediction error, respectively. Inter-axis coupling effects, i.e., the motion of one axis causing the dynamic responses of another axis, are correctly predicted. Also, the tracking error induced by motor ripple and nonlinear friction is correctly predicted as well. As the second contribution, the above proposed methodology is extended to also consider structural flexibility, which is a crucial behavior of large-sized industrial 5-axis machine tools. More importantly, structural vibration is nonlinear and posture-dependent due to the nature of a multibody system. In this thesis, prominent cases of flexibility-induced vibrations in a linear feed drive are studied and modeled by lumped mass-spring-damper system. Then, a flexible linear drive coupled with a rotary drive is systematically analyzed. It is found that the case with internal structural vibration between the linear and rotary drives requires an additional motion sensor for the proposed model identification method. This particular case is studied with an experimental setup. The thesis presents a method to reconstruct such missing internal structural vibration using the data from the embedded encoders as well as a low-cost micro-electromechanical system (MEMS) inertial measurement unit (IMU) mounted on the machine table. It is achieved by first synchronizing the data, aligning inertial frames, and calibrating mounting misalignments. Finally, the unknown internal vibration is reconstructed by comparing the rigid and flexible machine kinematic models. Due to the measurement limitation of MEMS IMUs and geometric assembly error, the reconstructed angle is unfortunately inaccurate. Nevertheless, the vibratory angular velocity and acceleration are consistently reconstructed, which is sufficient for the identification with reasonable model simplification. Finally, the reconstructed internal vibration along with the gathered servo data are used to identify the proposed machine dynamic model. Due to the separation of linear and nonlinear dynamics, the vibratory dynamics can be simply considered by adding complex pole pairs into the MIMO LTI system. Experimental validation shows that the identified model is able to predict the dynamic responses of the tracking error and motor force/torque to the input command trajectory and external disturbances, with 2% ~ 6% RMS error. Especially, the vibratory inter-axis coupling effect and posture-dependent effect are accurately depicted. Overall, this thesis presents a dynamic model-building approach for multi-axis feed drive assemblies. The proposed model is general and can be configured according to the kinematic configuration. The model-building approach only requires the data from the servo system or auxiliary motion sensors, e.g., an IMU, which is non-intrusive and in favor of industrial implementation. Future research includes further investigation of the IMU measurement, geometric error identification, validation using more complicated feed drive system, and applications to the planning and monitoring of 5-axis machining process