Controller Design for EMA in TVC Incorporating Force Feedback

The objective of this research was to develop control schemes and control design procedures for electromechanical actuators (EMA) in thrust vector control (TVC) applications. For a variety of reasons, there is a tendency within the aerospace community to use electromechanical actuators in applications where hydraulics have traditionally been employed. TVC of rocket engines is one such application. However, there is considerable research, development, and testing to be done before EMA will be accepted by the community at large for these types of applications. Besides the development of design procedures for the basic position controller, two major concerns are dealt with in this research by incorporating force feedback: 1) the effects of resonance on the performance of EMA-TVC-rocket-engine systems, and 2) the effects of engine start transients on EMA. This report only highlights the major contributions of this research

Application of unmanned aerial systems for high throughput phenotyping of large wheat breeding nurseries

Citation: Haghighattalab, A., Perez, L. G., Mondal, S., Singh, D., Schinstock, D., Rutkoski, J., . . . Poland, J. (2016). Application of unmanned aerial systems for high throughput phenotyping of large wheat breeding nurseries. Plant Methods, 12, 15. https://doi.org/10.1186/s13007-016-0134-6Background: Low cost unmanned aerial systems (UAS) have great potential for rapid proximal measurements of plants in agriculture. In the context of plant breeding and genetics, current approaches for phenotyping a large number of breeding lines under field conditions require substantial investments in time, cost, and labor. For field-based high-throughput phenotyping (HTP), UAS platforms can provide high-resolution measurements for small plot research, while enabling the rapid assessment of tens-of-thousands of field plots. The objective of this study was to complete a baseline assessment of the utility of UAS in assessment field trials as commonly implemented in wheat breeding programs. We developed a semi-automated image-processing pipeline to extract plot level data from UAS imagery. The image dataset was processed using a photogrammetric pipeline based on image orientation and radiometric calibration to produce orthomosaic images. We also examined the relationships between vegetation indices (VIs) extracted from high spatial resolution multispectral imagery collected with two different UAS systems (eBee Ag carrying MultiSpec 4C camera, and IRIS+ quadcopter carrying modified NIR Canon S100) and ground truth spectral data from hand-held spectroradiometer. Results: We found good correlation between the VIs obtained from UAS platforms and ground-truth measurements and observed high broad-sense heritability for VIs. We determined radiometric calibration methods developed for satellite imagery significantly improved the precision of VIs from the UAS. We observed VIs extracted from calibrated images of Canon S100 had a significantly higher correlation to the spectroradiometer (r = 0.76) than VIs from the MultiSpec 4C camera (r = 0.64). Their correlation to spectroradiometer readings was as high as or higher than repeated measurements with the spectroradiometer per se. Conclusion: The approaches described here for UAS imaging and extraction of proximal sensing data enable collection of HTP measurements on the scale and with the precision needed for powerful selection tools in plant breeding. Low-cost UAS platforms have great potential for use as a selection tool in plant breeding programs. In the scope of tools development, the pipeline developed in this study can be effectively employed for other UAS and also other crops planted in breeding nurseries

Parallel Tracking and Mapping for Controlling VTOL Airframe

This work presents a vision based system for navigation on a vertical takeoff and landing unmanned aerial vehicle (UAV). This is a monocular vision based, simultaneous localization and mapping (SLAM) system, which measures the position and orientation of the camera and builds a map of the environment using a video stream from a single camera. This is different from past SLAM solutions on UAV which use sensors that measure depth, like LIDAR, stereoscopic cameras or depth cameras. Solution presented in this paper extends and significantly modifies a recent open-source algorithm that solves SLAM problem using approach fundamentally different from a traditional approach. Proposed modifications provide the position measurements necessary for the navigation solution on a UAV. The main contributions of this work include: (1) extension of the map building algorithm to enable it to be used realistically while controlling a UAV and simultaneously building the map; (2) improved performance of the SLAM algorithm for lower camera frame rates; and (3) the first known demonstration of a monocular SLAM algorithm successfully controlling a UAV while simultaneously building the map. This work demonstrates that a fully autonomous UAV that uses monocular vision for navigation is feasible

Redesign of an Undergraduate Controls Laboratory with an Eye toward Accommodating Future Upgrades

This paper describes a recent and very advantageous upgrade of the undergraduate controls laboratory in the Mechanical and Nuclear Engineering Department at Kansas State University. The current lab has been in use for about a decade. Details regarding the original hardware fundamental to the laboratory activities, including the embedded digital signal processor (DSP) and the brushless DC motor stand (called the “Motorlab”), are presented and a summary of each of the fourteen weekly lab exercises is included. The DSP control program and PC user interface (UI) were each written in C. In order to accommodate future lab needs and to ease the maintenance burden, the embedded DSP was replaced by a PC running Real-Time LabVIEW from National Instruments (NI) and a NI data acquisition card. The original lab software was replaced with programs produced graphically in LabVIEW, known as Virtual Instruments (VIs). The VIs implement the controller on the real-time PC as well as the user interface on a host PC. Both the embedded DSP and the LabVIEW controllers are able to close the loop on the laboratory equipment with a 10 kHz, hard real-time, sample rate

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The core objective of this research is to develop an estimator capable of tracking the states of ground targets with observation measurements obtained from a single monocular camera mounted on a small unmanned aerial vehicle (UAV). Typical sensors on a small UAV include an inertial measurement unit (IMU) with three axes accelerometer and rate gyro sensors and a global positioning system (GPS) receiver which gives position and velocity estimates of the UAV. Camera images are combined with these measurements in state estimate filters to track ground features of opportunity and a target. The images are processed by a keypoint detection and matching algorithm that returns pixel coordinates for the features. Kinematic state equations are derived that reflect the relationships between the available input and output measurements and the states of the UAV, features, and target. These equations are used in the development of coupled state estimators for the dynamic state of the UAV, for estimation of feature positions, and for estimation of target position and velocity. The estimator developed is tested in MATLAB/SIMULINK, where GPS and IMU data are generated from the simulated states of a nonlinear model of a Navion aircraft. Images are als