A. James Clark School of Engineering
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Item WIND FRAME STATE ESTIMATION AND GUST REJECTION USING BIO-INSPIRED FLOW SENSORS(2015) Dean, William; Humbert, James S; Aerospace Engineering; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)Growing demand for robust, low-computation sensing and control of micro-air vehicles motivates development of new technology. A MEMS wind ow sensor has been developed in-house, drawing inspiration from setae structures seen in biology. The goal of this work is to validate the use of these new sensors for wind frame state estimation and gust rejection. Three of these sensors were mounted on the surface of a fuselage-like structure to estimate wind speed, angle of attack, and sideslip angle. Static linear and nonlinear estimation model structures and parameters were designed with time-domain equation-error system identication techniques. For small angles, state estimation was demonstrated for both estimation schemes. Gust rejection control was implemented to improve state regulation in the presence of a lateral gust stream. A robust controller was implemented and displayed lateral velocity and path perturbation attenuation.Item BIO-INSPIRED DISTURBANCE REJECTION WITH OCELLAR AND DISTRIBUTED ACCELERATION SENSING FOR SMALL UNMANNED AIRCRAFT SYSTEMS(2015) Gremillion, Gregory; Humbert, James Sean; Aerospace Engineering; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)Rapid sensing of body motions is critical to stabilizing a flight vehicle in the presence of exogenous disturbances as well as providing high performance tracking of desired control commands. This bandwidth requirement becomes more stringent as vehicle scale decreases. In many flying insects three simple eyes, known as the ocelli, operate as low latency visual egomotion sensors. Furthermore many flying insects employ distributed networks of acceleration-sensitive sensors to provide information about body egomotion to rapidly detect external forces and torques. In this work, simulation modeling of the ocelli visual system common to flying insects was performed based on physiological and behavioral data. Linear state estimation matrices were derived from the measurement models to form estimates of egomotion states. A fully analog ocellar sensor was designed and constructed based on these models, producing state estimation outputs. These analog state estimate outputs were characterized in the presence of egomotion stimuli. Feedback from the ocellar sensor, with and without complementary input from optic flow sensors, was implemented on a quadrotor to perform stabilization and disturbance rejection. The performance of the closed loop sensor feedback was compared to baseline inertial feedback. A distributed array of digital accelerometers was constructed to sense rapid force and torque measurements. The response of the array to induced motion stimuli was characterized and an automated calibration algorithm was formulated to estimate sensor position and orientation. A linear state estimation matrix was derived from the calibration to directly estimate forces and torques. The force and torque estimates provided by the sensor network were used to augment the quadrotor inner loop controller to improve tracking of desired commands in the presence of exogenous force and torque disturbances with a force-adaptive feedback control.