Interplanetary multiple gravity assist (MGA) trajectory optimization has long been a field of interest to space scientists and engineers. Gravity assist maneuvers alter a spacecraft's velocity vector and potentially allow spacecraft to achieve changes in velocity which would otherwise be unfeasible given our current technological limitations. Unfortunately, designing MGA trajectories is difficult and in order to find good solutions, deep space maneuvers (DSM) are often required which further increase the complexity of the problem. In addition, despite the active research in the field over the last 50 years, software for MGA trajectory optimization is scarce. A few good commercial, and even fewer open-source, options exist, but a majority of quality software remains proprietary. The intent of this thesis is twofold. The first part of this work explores the realm of global optimization applied to multiple gravity assist trajectories with deep space maneuvers (MGA-DSM). With the constant influx of new global optimization algorithms and heuristics being developed in the global optimization community, this work aims to be a high level optimization approach which makes use of those algorithms instead of trying to be one itself. Central to this approach is PaGMO, which is the open-source Parallel Multiobjective Global Optimizer created by ESA's Advanced Concepts Team (ACT). PaGMO is an implementation of the Island Model Paradigm which allows the parallelization of different global optimizers. The second part of this work introduces the IGATO software which improves PaGMO by complementing it with dynamic restart capabilities, a pruning algorithm which learns over time, subdomain decomposition, and other techniques to create a powerful optimization tool. IGATO aims to be an open-source platform independent C++ application with a robust graphical user interface (GUI). The application is equipped with 2D plotting and simulations, real time Porkchop Plot generation, and other useful features for analyzing various problems. The optimizer is tested on several challenging MGA-DSM problems and performs well: consistently performing as well or better than PaGMO on its own.

Proper engine selection for Reusable Launch Vehicles (RLVs) is a key factor in the design of low cost reusable launch systems for routine access to space. RLVs typically use combinations of different types of engines used in sequence over the duration of the flight. Also, in order to properly choose which engines are best for an RLV design concept and mission, the optimal trajectory that maximizes or minimizes the mission objective must be found for that engine configuration. Typically this is done by the designer iteratively choosing engine combinations based on his/her judgment and running each individual combination through a full trajectory optimization to find out how well the engine configuration performed on board the desired RLV design. This thesis presents a new method to reliably predict the optimal engine configuration and optimal trajectory for a fixed design of a conceptual RLV in an automated manner. This method is accomplished using the original code Steele-Flight. This code uses a combination of a Genetic Algorithm (GA) and a Non-Linear Programming (NLP) based trajectory optimizer known as GPOPS II to simultaneously find the optimal engine configuration from a user provided selection pool of engine models and the matching optimal trajectory. This method allows the user to explore a broad range of possible engine configurations that they wouldn't have time to consider and do so in less time than if they attempted to manually select and analyze each possible engine combination. This method was validated in two separate ways. The codes ability to optimize trajectories was compared to the German trajectory optimizer suite known as ASTOS where only minimal differences in the output trajectory were noticed. Afterwards another test was performed to verify the method used by Steele-Flight for engine selection. In this test, Steele-Flight was provided a vehicle model based on the German Saenger TSTO RLV concept and models of turbofans, turbojets, ramjets, scramjets and rockets. Steele-Flight explored the design space through the use of a Genetic Algorithm to find the optimal engine combination to maximize payload. The results output by Steele-Flight were verified by a study in which the designer manually chose the engine combinations one at a time, running each through the trajectory optimization routine to determine the best engine combination. For the most part, these methods yielded the same optimal engine configurations with only minor variation. The code itself provides RLV researchers with a new tool to perform conceptual level engine selection from a gathering of user provided conceptual engine data models and RLV structural designs and trajectory optimization for fixed RLV designs and fixed mission requirement. / Master of Science

The objectives of this study were to implement force sensors on the feet of a bipedal robot and analyze their response at different conditions. The data will be used to design a control strategy for the robot. The powered joints of the robot are driven by linear motors. A force sensor circuit was made and calibrated with different kinds of weight. A trajectory generator and inverse kinematics calculator for the robot were made to control the robot walking movement in an open-loop manner. The force data were taken at a certain period of time when the robot was in a standing position. Experiments with external disturbances were also performed on the robot. The ZMP position and mass of the robot were calculated by using the data of force sensors. The force sensor circuit was reliable in taking and handling the data from the sensor although the noise from the motors of the robot was present. / <p>Validerat; 20111115 (anonymous)</p>

Yes / This paper presents an iterative learning scheme for vision guided robot trajectory tracking. At first, a stability criterion for designing iterative learning controller is proposed. It can be used for a system with initial resetting error. By using the criterion, one can convert the design problem into finding a positive definite discrete matrix kernel and a more general form of learning control can be obtained. Then, a three-dimensional (3-D) trajectory tracking system with a single static camera to realize robot movement imitation is presented based on this criterion.

Yes / This paper proposes a novel trajectory tracking control approach for nonholonomic wheeled mobile robots. In this approach, the integration of feed-forward and feedback controls is presented to design the kinematic controller of wheeled mobile robots, where the control law is constructed on the basis of Lyapunov stability theory, for generating the precisely desired velocity as the input of the dynamic model of wheeled mobile robots; a proportional-integral-derivative based membrane controller is introduced to design the dynamic controller of wheeled mobile robots to make the actual velocity follow the desired velocity command. The proposed approach is defined by using an enzymatic numerical membrane system to integrate two proportional-integral-derivative controllers, where neural networks and experts’ knowledge are applied to tune parameters. Extensive experiments conducted on the simulated wheeled mobile robots show the effectiveness of this approach. / The work of XW and GZ is supported by the National Natural Science Foundation of China (61170016, 61373047). The work of MG, FI and RL was supported by a grant of the Romanian National Authority for Scientific Research, CNCS-UEFISCDI (project number: PN-II-ID-PCE-2011-3-0688).

From 2001-2007, students from India have consistently comprised the largest ethnic group of international students on college campuses across the United States (Open Doors: Report on International Educational Exchange, 2007). Despite a number of studies that have researched the mental health of international students in the U.S., none have done so primarily with Indian graduate students. Theoretical and empirical literature regarding the psychological changes and acculturation patterns that international students undergo after their transition do not explore the possibility of multiple pathways of change. The current study identified four separate mental health trajectories for Indian international graduate students during their first year in the U.S. It also found three distinct patterns of acculturation for the Indian culture and four acculturation trajectories for the European American culture. The size of one's adjustment, feelings about transition, gender role attitudes, and availability of out-group support were all significant contributors to the variability among empirically derived mental health trajectories.

There is an overemphasis on procedures and manipulation of symbols in calculus and not enough emphasis on conceptual understanding of the subject. Specifically, students struggle to understand and correctly apply concepts in calculus such as the chain rule, implicit differentiation, and related rates. Students can learn mathematics more deeply when they make connections between different mathematical ideas. I have hypothesized that students can make powerful connections between the chain rule, implicit differentiation, and related rates through the mathematical concept of nested multivariation. Based on this hypothesis, I created a hypothetical learning trajectory (HLT) rooted in nested multivariation for students to develop an understanding of these three concepts. In this study, I explore my HLT through a small-scale teaching experiment with individual first-semester calculus students using tasks based on the HLT.Based on the teaching experiment, nested multivariational reasoning proved to be critical in understanding how the variables within a function composition change together and in developing intuition and understanding for the multiplicative nature of the chain rule. Later, nested multivariational reasoning was mostly important in recognizing the existence of a nested relationship and the need to use the chain rule in differentiation. Overall, through the HLT, students gained a connected and conceptual understanding for the chain rule, implicit differentiation, and related rates. I also discuss how the HLT might be adjusted and improved for future use.

<p dir="ltr">The electrification of pneumatic or hydraulic system on aircraft has been shown effective in reducing the fuel burn. Recently, electrifying propulsive loads has attracted a lot of atten- tion to further improve fuel economy. This work focuses on tools to facilitate more electric aircraft at conceptual design stage, particularly assuming a turbo-generator architecture. Specifically, we develop a simulation tool, mimicking SUAVE [1], which allows mission and fuel burn analysis. Major differences from SUAVE include more detailed models of compo- nents in the electric propulsive branch and degrees of freedom to adjust the velocity profile along the entire mission. Based on the simulator, this work further proposes to leverage a gradient-free optimization technique, which optimizes the optimal velocity profile along the entire mission to minimize fuel burn. Simulation results on two aircraft designs, a con- ventional Boeing 737-800 and NASA-STARC-ABL, verify the effectiveness of the proposed tools.</p>

This work presents comprehensive analytics of trajectories of functional loss and recovery using sequence analysis and clustering techniques. The study focuses on a large dataset consisting of assessments of activities of daily living conducted among nursing home residents. The first main part of this research involves converting the assessments into sequences of disability combinations and utilizing graphical tools and various indicators to gain valuable insights into the trajectories of functional disabilities over time. In the second part of the research, a novel clustering approach is introduced that combines Markov models with distance-based techniques. This hybrid methodology results in 13 distinct clusters of trajectories. The clusters are thoroughly examined, and representative sets are carefully selected based on various criteria. This selection process ensures that the chosen sets accurately represent the characteristics of each cluster. The findings of this study have significant implications for healthcare systems, including developing predictive models which can be utilized to forecast the trajectory of individual patients based on their cluster membership. This enables healthcare providers to anticipate disease progression, tailor treatments, and dynamically adjust care plans, resulting in improved patient outcomes and the overall quality of care. Moreover, the information derived from the analytics can aid in optimizing healthcare systems by facilitating resource allocation and cost optimization. The insights gained can also guide policymakers and families in planning appropriate care for patients. This research advances healthcare decision-making and ensures appropriate support. / Thesis / Master of Science (MSc) / The ability to independently perform activities of daily living (ADLs) is a crucial indicator of an individual's health status, and the loss of this ability can have a profound impact on their overall quality of life. Our research focuses on analyzing the trajectories of patients as they experience functional decline and recovery. While various techniques have been utilized to explore ADL trajectories, this study stands out by employing clustering and sequence analysis approaches to examine different groups of trajectories. To overcome the computational challenges involved, we propose a combined clustering approach. This hybrid approach consists of two phases: applying a Markov model prior to distance-based algorithms. The findings derived from our research hold significant applications in optimizing healthcare systems, improving health outcomes, facilitating the development of targeted and effective interventions that support patients in preserving their independence, and enhancing the quality of care.

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