Passive Stability And Actuation Of Micro Aerial Vehicles

Micro Aerial Vehicles (MAVs) have increased in popularity in recent years. The most common platform, the quadrotor, has surpassed other MAVs like traditional helicopters and ornithopters in popularity mainly due to their simplicity. Yet the quadrotor design is a century old and was intended to carry people. We set out to design a MAV that is designed specifically to be a MAV, i.e. a vehicle not intended to carry humans as a payload. With this constraint lifted the vehicle can continuously rotate, which would dizzy a human, can sustain larger forces, which would damage a human, or can take advantage of scaling properties, where it may not work at human scale. Furthermore, we aim for simplicity by removing vehicle controllers and reducing the number of actuators, such that the vehicle can be made cost effective, if not disposable. We begin by studying general equations of motion for hovering MAVs. We search for vehicle configurations that exhibit passive stability, allowing the MAV to operate without a controller or actuators to apply control, ideally a single actuator. The analysis suggests two distinct types of passively stabilized MAVs and we create test vehicles for both. With simple hovering achieved, we concentrate on controlled motion with an emphasis on doing so without adding actuators. We find we can attain three degree of freedom control using separation of time scales with our actuator via low frequency for control in the vertical direction and high frequency for control in the horizontal plane. We explore techniques for achieving high frequency actuator control, which also allow the compensation of motor defects, specifically cogging torque. We combine passive stability with the motion control into two vehicles, UNO and Piccolissimo. UNO, the Underactuated-propeller Naturally-stabilized One-motor vehicle, demonstrates the capabilities of simple vehicles by performing maneuvers like conventional quadrotors. Piccolissimo, Italian for “very little”, demonstrates the merits of passive stability and single actuator control by being the smallest, self-powered, controllable MAV

3D locomotion biomimetic robot fish with haptic feedback

This thesis developed a biomimetic robot fish and built a novel haptic robot fish system based on the kinematic modelling and three-dimentional computational fluid dynamic (CFD) hydrodynamic analysis. The most important contribution is the successful CFD simulation of the robot fish, supporting users in understanding the hydrodynamic properties around it

Nonlinear stability and control study of highly maneuverable high performance aircraft, phase 2

This research should lead to the development of new nonlinear methodologies for the adaptive control and stability analysis of high angle-of-attack aircraft such as the F18 (HARV). The emphasis has been on nonlinear adaptive control, but associated model development, system identification, stability analysis and simulation is performed in some detail as well. Various models under investigation for different purposes are summarized in tabular form. Models and simulation for the longitudinal dynamics have been developed for all types except the nonlinear ordinary differential equation model. Briefly, studies completed indicate that nonlinear adaptive control can outperform linear adaptive control for rapid maneuvers with large changes in alpha. The transient responses are compared where the desired alpha varies from 5 degrees to 60 degrees to 30 degrees and back to 5 degrees in all about 16 sec. Here, the horizontal stabilator is the only control used with an assumed first-order linear actuator with a 1/30 sec time constant

Nonlinear stability and control study of highly maneuverable high performance aircraft

This project is intended to research and develop new nonlinear methodologies for the control and stability analysis of high-performance, high angle-of-attack aircraft such as HARV (F18). Past research (reported in our Phase 1, 2, and 3 progress reports) is summarized and more details of final Phase 3 research is provided. While research emphasis is on nonlinear control, other tasks such as associated model development, system identification, stability analysis, and simulation are performed in some detail as well. An overview of various models that were investigated for different purposes such as an approximate model reference for control adaptation, as well as another model for accurate rigid-body longitudinal motion is provided. Only a very cursory analysis was made relative to type 8 (flexible body dynamics). Standard nonlinear longitudinal airframe dynamics (type 7) with the available modified F18 stability derivatives, thrust vectoring, actuator dynamics, and control constraints are utilized for simulated flight evaluation of derived controller performance in all cases studied

From Rousettus aegyptiacus (bat) Landing to Robotic Landing: Regulation of CG-CP Distance Using a Nonlinear Closed-Loop Feedback

Bats are unique in that they can achieve unrivaled agile maneuvers due to their functionally versatile wing conformations. Among these maneuvers, roosting (landing) has captured attentions because bats perform this acrobatic maneuver with a great composure. This work attempts to reconstruct bat landing maneuvers with a Micro Aerial Vehicle (MAV) called Allice. Allice is capable of adjusting the position of its Center of Gravity (CG) with respect to the Center of Pressure (CP) using a nonlinear closed-loop feedback. This nonlinear control law, which is based on the method of input-output feedback linearization, enables attitude regulations through variations in CG-CP distance. To design the model-based nonlinear controller, the Newton-Euler dynamic model of the robot is considered, in which the aerodynamic coefficients of lift and drag are obtained experimentally. The performance of the proposed control architecture is validated by conducting several experiments

IMPLEMENTATION OF KALMAN FILTER TO TRACKING CUSTOM FOUR-WHEEL DRIVE FOUR-WHEEL-STEERING ROBOTIC PLATFORM

Vehicle tracking is an important component of autonomy in the robotics field, requiring integration of hardware and software, and the application of advanced algorithms. Sensors are often plagued with noise and require filtering. Additionally, no single sensor is sufficient for effective tracking. Data from multiple sensors is needed in order to perform effective tracking. The Kalman Filter provides a convenient and efficient solution for filtering and fusing sensor data as well as estimating noise error covariances. Consequently, it has been essential in tracking algorithms since its introduction in 1960. This thesis presents an application of the Kalman filter to tracking of a custom four-wheel-drive four-wheel-steering vehicle using a limited sensor suite. Sensor selection is discussed, along with the characteristics of the sensor noise as related to meeting the requirements of the Kalman filter for guaranteeing optimality. The filter requires the development of a dynamical model, which is derived using empirical data methods and evaluated. Tracking results are presented and compared to unfiltered data