In this work, the data analysis of oscillating flapping fins is conducted for mathematical model. Data points of heave and surge force obtained by the CFD (Computational Fluid Dynamics) for different geometrical kinds of flapping fins. The fin undergoes a combination of vertical and angular oscillatory motion, while travelling at constant forward speed. The surge thrust and heave lift are generated by the combined motion of the flapping fins, especially due to the carrier vehicle’s heave and pitch motion will be investigated to acquire system identification with CFD data available while the fin pitching motion is selected as a function of fin vertical motion and it is imposed by an external mechanism. The data series applied to model unsteady lifting flow around the system will be employed to develop an optimization algorithm to establish an approximation transfer function model for heave force and obtain a predicting black box system with nonlinear theory for surge force with fin motion control synthesis.

This thesis presents a real-time autonomous guidance and control method for a quadrotor in a GPS-denied environment. The quadrotor autonomously seeks a destination while it avoids obstacles whose shape and position are initially unknown. We implement the obstacle avoidance and destination seeking methods using off-the-shelf sensors, including a vision-sensing camera. The vision-sensing camera detects the positions of points on the surface of obstacles. We use this obstacle position data and a potential-field method to generate velocity commands. We present a backstepping controller that uses the velocity commands to generate the quadrotor's control inputs. In indoor experiments, we demonstrate that the guidance and control methods provide the quadrotor with sufficient autonomy to fly point to point, while avoiding obstacles.

Several targeting algorithms are developed and analyzed for possible future use onboard a spacecraft. Each targeter is designed to determine the appropriate propulsive burn for translunar injection to obtain desired orbital parameters upon arrival at the moon. Primary design objectives are to minimize the computational requirements for each algorithm but also to ensure reasonable accuracy, so that the algorithm’s errors do not force the craft to conduct large mid-course corrections. Several levels of accuracy for dynamical models are explored, the convergence range and speed of each algorithm are compared, and the possible benefits of the Broyden and trust-region targeters are evaluated. These targeters provide a proof of concept for the feasibility of a translunar injection targeting algorithm. Anticipating some future improvements, these algorithms could serve as a viable alternative to uploading ground-based targeting solutions and bypass the problems of delays and disruptions in communication, enabling the craft to conduct a translunar injection burn autonomously.

Thesis (M.A.)--Miami University, Dept. of Psychology, 2009. / Title from first page of PDF document. Includes bibliographical references (p. 43-47).

<div>Optimization of atmospheric flight control has long been performed on the ground, prior to mission flight due to large computational requirements used to solve non-linear programming problems. Onboard trajectory optimization enables the creation of new reference trajectories and updates to guidance coefficients in real time. This thesis summarizes the methods involved in solving optimal control problems in real time using convexification and Sequential Convex Programming (SCP). The following investigation provided insight in assessing the use of state of the art SCP optimization architectures and convexification of the hypersonic equations of motion[ 1 ]–[ 3 ] with different control schemes for the purposes of enabling on-board trajectory optimization capabilities.</div><div>An architecture was constructed to solve convexified optimal control problems using direct population of sparse matrices in triplet form and an embedded conic solver to enable rapid turn around of optimized trajectories. The results of this show that convexified optimal control problems can be solved quickly and efficiently which holds promise in autonomous trajectory design to better overcome unexpected environments and mission parameter changes. It was observed that angle of attack control problems can be successfully convexified and solved using SCP methods. However, the use of multiple coupled controls is not guaranteed to be successful with this method when they act in the same plane as one another. The results of this thesis demonstrate that state of the art SCP methods have the capacity to enable onboard trajectory optimization with both angle of attack control and bank angle control schemes.</div><div><br></div>

Space debris growth and an influx in space traffic will create a need for increased space traffic management. Due to orbital population density and likely future growth, the implementation of a slot architecture to Sun-synchronous orbit is considered in order to mitigate conjunctions among active satellites. This paper furthers work done in Sun-synchronous orbit slot architecture design and focuses on two main aspects. First, an in-depth relative motion analysis of satellites with respect to their assigned slots is presented. Then, a method for developing a slot architecture from a specific set of user defined inputs is derived.

Currently, the South Pole has a large data problem. It is estimated that 1.2 TB of data is being produced every day, but less than 500 GB of that data is being uploaded via aging satellites to researchers in other parts of the world. This requires those at the South Pole to analyze the data and carefully select the parts to send, possibly missing out on vital scientific information. The South Pole Carrier Pigeon will look to bridge this data gap. The Carrier Pigeon will be a small unmanned aerial vehicle that will carry a 30 TB solid-state hard drive from the South Pole to various destinations in the Southern Hemisphere, but it has been designed to y to Christchurch, New Zealand. This 87 lb. UAV will be able to y 3,650 nmi. up to 25,000 ft., using a 5.7 hp. engine. It will feature an de-icing system on the leading edge of its 8 ft. span wing to allow it to y through cold, moist climates. It will have a 39 in. long fuselage with a tail boom of 33 in. The aircraft has been designed to be made out of composites, thus reducing both the weight of the aircraft as well as its drag. It has been designed to come apart in order to be shipped successfully to the South Pole. There, it will be assembled and launched via a custom pneumatic launcher. It will y autonomously to 15,000 ft. and cruise climb throughout the flight to 25,000 ft., before descending to its destination. There, it will be caught by a net restraint system, where the hard drive will be extracted. The Carrier Pigeon is truly a unique vehicle for its size, range, and robustness.

Aerocapture offers a near propellantless and quick method of orbit insertion at atmosphere bearing planetary destinations. Compared to conventional propulsive insertion, the primary advantage of using aerocapture is the savings in propellant mass which could be used to accommodate more useful payload. To protect the spacecraft from the aerodynamic heating during the maneuver, the spacecraft must be enclosed in a protective aeroshell or deployable drag device which also provides aerodynamic control authority to target the desired conditions at atmospheric exit. For inner planets such as Mars and Venus, aerocapture offers a very attractive option for inserting small satellites or constellations into very low circular orbits such as those used for imaging or radar observations. The large amount of propellant required for orbit insertion at outer planets such as Uranus and Neptune severely limits the useful payload mass that can delivered to orbit as well as the achievable flight time. For outer planet missions, aerocapture opens up an entirely new class of short time of flight trajectories which are infeasible with propulsive insertion. A systems framework for rapid conceptual design of aerocapture missions considering the interdependencies between various elements such as interplanetary trajectory and vehicle control performance for aerocapture is presented. The framework provides a step-by-step procedure to formulate an aerocapture mission starting from a set of mission objectives. At the core of the framework is the ``aerocapture feasibility chart", a graphical method to visualize the various constraints arising from control authority requirement, peak deceleration, stagnation-point peak heat rate, and total heat load as a function of vehicle aerodynamic performance and interplanetary arrival conditions. Aerocapture feasibility charts have been compiled for all atmosphere-bearing Solar System destinations for both lift and drag modulation control techniques. The framework is illustrated by its application to conceptual design of a Venus small satellite mission and a Flagship-class Neptune mission using heritage blunt-body aeroshells. The framework is implemented in the Aerocapture Mission Analysis Tool (AMAT), a free and open-source Python package, to enable scientists and mission designers perform rapid conceptual design of aerocapture missions. AMAT can also be used for rapid Entry, Descent, and Landing (EDL) studies for atmospheric probes and landers at any atmosphere-bearing destination.

Electric and hybrid-electric vehicles lean heavily on intricate control algorithms to provide smooth, reliable, and secure operations under any driving conditions. Three distinct supervisory control strategies have been developed, each aiming to improve reliability and vehicle performance of a dual-motor electric vehicle equipped with an all-wheel-drive, fully electric powertrain. These algorithms are adept at dynamically modulating and constraining the torque provided to the wheels, leveraging two autonomous permanent magnet electric drive units. This study utilizes a vehicle model jointly provided by MathWorks and General Motors in partnership with industry sponsors. The these strategies were implemented in the model and enhanced the performance, vehicle range, energy consumption, regenerated energy using specific EDUs provided by sponsors. Adhering to a systematic engineering iterative method, the emphasis was heavily placed on simulation and modeling during the development and validation of these strategies. Simulations ensured robust testing before field implementation, emphasizing software modeling's vital role.

Vehicle-to-infrastructure (V2I) and vehicle-to-vehicle (V2V) communication enable the sharing, in real time, of vehicular locations and speeds with other vehicles, traffic signals, and traffic control centers. This shared information can help traffic to better traverse intersections, road segments, and congested neighborhoods, thereby reducing travel times, increasing driver safety, generating data for traffic planning, and reducing vehicular pollution. This study, which focuses on vehicular pollution, used an analysis of data from NREL, BTS, and the EPA to determine that the widespread use of V2V-based truck platooning—the convoying of trucks in close proximity to one another so as to reduce air drag across the convoy—could eliminate 37.9 million metric tons of CO2 emissions between 2022 and 2026.