Continuous Descent Arrival (CDA) is a procedure where aircraft descend, at or near idle thrust, from their cruise altitude to their Final Approach Fix without leveling off. By eliminating inefficient leveling off at low altitude, CDA provides benefits such as fuel savings, flight time savings, and the significant noise reduction near airports, but the usage of CDAs has been limited in low traffic condition due to difficulty in the separation management. For the successful CDA without degradation of the runway throughput, air traffic controllers should know the performance bound of the CDA trajectory and control the time of arrival for each aircraft, which is interpreted as Required Time of Arrival (RTA) from the aircraft standpoint. This thesis proposes a novel trajectory optimization methodology to meet RTA constraint. The CDA trajectory optimization problem in the flight management system is modeled as a path constrained optimal control problem of switched dynamical system. A sequential method that performs mode sequence estimation and parameter optimization, sequentially, is proposed to solve this problem. By analyzing the relaxed optimal solution with simplified dynamics, a computationally efficient algorithm to find the optimal switching structure is proposed and applied for the mode sequence estimation. This thesis also proposes a performance-bound analysis methodology using optimal control techniques to help controllers make a feasible schedule for CDA operations at a meter fix. The feasible time range analysis for a wide variety of aircraft is performed by using the proposed methodology. Based on the analysis result, a single flight time strategy is proposed for the application of CDA in high traffic conditions. The simulation with real traffic data has been shown that the single flight time strategy, combined with the proposed fixed RTA trajectory optimization, guarantees the conflict free CDA operation.

<p>Constrained optimization is used to derive human postures and movements. In the first study a static 3D model with 30 muscle groups is used to analyse postures. The activation levels of these muscles are minimized in order to represent the individual's choice of posture. Subject specific data in terms of anthropometry, strength and orthopedic aids serve as input. The aim is to study effects from orthopedic treatment and altered abilities of the subject. Initial validation shows qualitative agreement of posture strategies but further details about passive stiffness and anthropometry are needed, especially to predict pelvis orientation. In the second application, the athletic long jump, a problem formulation is developed to find optimal movements of a multibody system when subjected to contact. The model was based on rigid links, joint actuators and a wobbling mass. The contact to the ground was modelled as a spring-damper system with tuned properties. The movement in the degrees of freedom representing physical joints was described over contact time through two fifth-order polynomials, with a variable transition time, while the motion in the degrees of freedom of contact and wobbling mass was integrated forwards in time, as a consequence. Muscle activation variables were then optimized in order to maximize ballistic flight distance. The optimization determined contact time, end configuration, activation and interaction with the ground from an initial configuration. The results from optimization show a reasonable agreement with experimentally recorded jumps, but individual recordings and measurements are needed for more precise conclusions.</p><p> </p>

This thesis explores how an Asteroid Sample Return Mission might make use of solar electric propulsion to send a spacecraft on a journey to the asteroid 1989ML and back. It examines different trajectories that can be used to get an asteroid sample return or similar spacecraft to an interplanetary destination and back in the most fuel-efficient manner. While current plans call for keeping such a spacecraft on the asteroid performing science experiments for approximately 90 days, it is prudent to inquire how lengthening or shortening this time period may affect mission fuel requirements. Using optimal control methods, various mission scenarios have been modeled and simulated. The results suggest that the amount of time that the spacecraft may spend on the asteroid surface can be approximated as a linear function of the available fuel mass. Furthermore, It can be shown that as maximum available thrust is decreased, the radial component of the optimal thrust vector becomes more pronounced.

Trajectory planning is an important component of autonomous driving. It takes the result of route-level navigation plan and generates the motion-level commands that steer an autonomous passenger vehicle (APV). Prior work on solving this problem uses either a sampling-based or optimization-based trajectory planner, accompanied by some high-level rule generation components.

Forestry machines have the power to lift heavy logs, but they are not so smart at providing information, or help operators perform better work. The main reason to this problem is the low level of technology applied to forestry machines, which has not changed so much since the forestry machines were first introduced in the 1960’s. But starting 2013, machines manufacturers got inspired by developments in the automation and robotics industry, several of new technologies have been developed in the market - computerized hydraulics, feedback controllers for vibration damping, sensor-based motion control systems, improvements in mechanical design, smart suspension controller, etc. Largely, this development is attributed to better hardware and software developed during the last decade by researchers of Scandinavian institutes. In this thesis, we introduce a new type of forestry machine, the harwarder, which can perform the work of two machines (harvester and forwarder) by a single one. The forwarder is a forestry vehicle that carries big felled logs. The harvester is a type of heavy forestry manipulator employed in cut-to-length logging operations for felling, and bucking trees. Both the manipulator and vehicle should work synchronized to get the best out of this design. To benefit out of its design, in the first part of thesis we will analyze the kinematics and dynamics of machine, and design a time optimal coordinated motion via virtual holonomic constraints, to solve a particular task of forestry crane. The second part consists on applying optimization to reduce energy consumption during the motion. Result of thesis work: 1) By using coordinated motion, consequently the energy consumptions are drastically reduced comparing to traditional motion of the crane. 2) By applying optimization, the energy efficiency is improved.

Asteroid mining is rapidly becoming a popular topic amongst space community, primarily due to the potential resources that the asteroids can provide for future spacefaring. One of the interesting resources that can be obtained from asteroids is water, which can also be processed into oxygen and fuel. An intriguing concept would be to process fuel from asteroid, and establish a fuel depot in an Earth-centered orbit. This thesis considers a mission concept consisting of travelling to an Arjuna near-Earth asteroid from a lunar distant retrograde orbit as a depot orbit, processing fuel in-situ from the water on the asteroid, and bringing back 100 tons of fuel to the depot orbit. In order to minimize fuel consumption for such a trip, the thesis develops an optimization method that can obtain the best trajectory for different phases of the round trip, given certain constraints to ensure the spacecraft successfully reaches the asteroid and comes back to the Earth system. The optimization model consists of four steps, i.e., the outbound trip, the first phase of the return trip, the second phase of the return trip, and the optimization for the combined phases of return trip. The outbound trip is the trajectory from the depot orbit to the asteroid. After at least three months of mining, the spacecraft brings back the processed fuel to the vicinity of the Moon. This phase is called the first phase of the return trip. The spacecraft is then captured without an insertion burn to an Earth-centered orbit by a lunar gravity assist maneuver, and travels to the point where the insertion maneuver to the depot orbit begins. This is the second phase of the return trip. The last step of the optimization is the combination of the two phases of return trip, in addition to the final maneuver for entering the lunar distant retrograde orbit. The optimization method uses MATLAB fmincon solver, and it was applied to 29 synthetic asteroids. There were 19 converged solutions, but for 10 asteroids the optimizations was not able to converge. The lowest minimum fuel consumption for a trip is 19965.5 kg, and the highest minimum fuel consumption is 61821.4 kg. For the lowest minimum fuel consumption, the duration of the trip is nearly 7 years, and the duration for the highest minimum fuel consumption is about 2.6 years.

Thesis (M.S.)--University of Missouri-Columbia, 2006. / The entire dissertation/thesis text is included in the research.pdf file; the official abstract appears in the short.pdf file (which also appears in the research.pdf); a non-technical general description, or public abstract, appears in the public.pdf file. Title from title screen of research.pdf file viewed on (February 23, 2007) Includes bibliographical references.

Optimization has been used to simulate human neural control and resulting movement patterns. The short term aim was to develop the methodology required for solving the movement optimization problem often arising when modelling human movements. A long term aim is the contribution to increased knowledge about various human movements, wherein postures is one specific case. Simulation tools can give valuable information to improve orthopeadic treatments and technique for training and performance in sports. In one study a static 3D model with 30 muscle groups was used to analyse postures. The activation levels of these muscles are minimized in order to represent the individual’s choice of posture. Subject specific data in terms of anthropometry, strength and orthopedic aids serve as input. The specific aim of this part was to study effects from orthopedic treatment and altered abilities of the subject. Initial validation shows qualitative agreement of posture strategies but further details about passive stiffness and anthropometry are needed, especially to predict pelvis orientation. Four studies dealt with movement optimization. The main methodological advance was to introduce contact constraints to the movement optimization. A freetime multiple phase formulation was derived to be able to analyse movements where different constraints and degrees of freedom are present in subsequent phases of the movements. The athletic long jump, a two foot high jump, a backward somersault and rowing were used as applications with their different need of formulation. Maximum performance as well as least effort cost functions have been explored. Even though it has been a secondary aim in this work the results show reasonable agreement to expected movements in reality. Case specific subject properties and inclusion of muscle dynamics are required to draw conclusions about improvements in the sport activity, respectively. / <p>QC 20120910</p>

There are occasions when it is preferable that an aircraft flies asclose to the ground as possible. It is difficult for a pilot to predict the topography when he cannot see beyond the next hill, and this makes it hard for him to find the optimal flight trajectory. With the help of a terrain database in the aircraft, the forthcoming topography can be found in advance and a flight trajectory can be calculated in real-time. The main goal is to find an optimal control sequence to be used by the autopilot. The optimization algorithm, which is created for finding the optimal control sequence, has to be run often and therefore, it has to be fast. This thesis presents a terrain following algorithm based on Model Predictive Control which is a promising and robust way of solving the optimization problem. By using trajectory optimization, a trajectory which follows the terrain very good is found for the non-linear model of the aircraft.

Modular and Reconfigurable Robots (MRRs) are those designed to address the increasing demand for flexible and versatile manipulators in manufacturing facilities. The term, modularity, indicates that they are constructed by using a limited number of interchangeable standardized modules which can be assembled in different kinematic configurations. Thereby, a wide variety of specialized robots can be built from a set of standard components. The term, reconfigurability, implies that the robots can be disassembled and rearranged to accommodate different products or tasks rather than being replaced. A set of MRR modules may consist of joints, links, and end-effectors. Different kinematic configurations are achieved by using different joint, link, and end-effector modules and by changing their relative orientation. The number of distinct kinematic configurations, attainable by a set of modules, varies with respect to the size of the module set from several tens to several thousands. Although determining the most suitable configuration for a specific task from a predefined set of modules is a highly nonlinear optimization problem in a hybrid continuous and discrete search space, a solution to this problem is crucial to effectively utilize MRRs in manufacturing facilities. The objective of this thesis is to develop novel optimization methods that can effectively search the Kinematic Configuration (KC) space to identify the most suitable manipulator for any given task. In specific terms, the goal is to develop and synthesize fast and efficient algorithms for a Task-Based Configuration Optimization (TBCO) from a given set of constraints and optimization criteria. To achieve such efficiency, a TBCO solver, based on Memetic Algorithms (MA), is proposed. MAs are hybrids of Genetic Algorithms (GAs) and local search algorithms. MAs benefit from the exploration abilities of GAs and the exploitation abilities of local search methods simultaneously. Consequently, MAs can significantly enhance the search efficiency of a wide range of optimization problems, including the TBCO. To achieve more optimal solutions, the proposed TBCO utilizes all the solutions of the Inverse Kinematics(IK) problem. Another objective is to develop a method for incorporating the multiple solutions of the IK problem in a trajectory optimization framework. The output of the proposed trajectory optimization method consists of a sequence of desired tasks and a single IK solution to reach each task point. Moreover, the total cost of the optimized trajectory is utilized in the TBCO as a performance measure, providing a means to identify kinematic configurations with more efficient optimized trajectories. The final objective is to develop novel IK solvers which are both general and complete. Generality means that the solvers are applicable to all the kinematic configurations which can be assembled from the available module inventory. Completeness entails the algorithm can obtain all the possible IK solutions.