Aerospace Engineeringhttp://hdl.handle.net/1903/22062021-11-30T09:31:24Z2021-11-30T09:31:24ZCONTROLLER SYNTHESIS AND FORMAL BEHAVIOR INFERENCE IN AUTONOMOUS SYSTEMSCarrillo, Estefanyhttp://hdl.handle.net/1903/277682021-09-16T07:47:55Z2021-01-01T00:00:00ZCONTROLLER SYNTHESIS AND FORMAL BEHAVIOR INFERENCE IN AUTONOMOUS SYSTEMS
Carrillo, Estefany
Autonomous systems are widely used in crucial applications such as surveillance,defense, reghting, and search & rescue operations. Many of these application
require systems to satisfy user-dened requirements describing the desired
system behavior. Given high-level requirements, we are interested in the design of
controllers that guarantee the compliance of these requirements by the system. However,
ensuring that these systems satisfy a given set of requirements is challenging
for many reasons, one of which is the large computational cost incurred by having
to account for all possible system behaviors and environment conditions. These
computational diculties are exacerbated when systems are required to satisfy requirements
involving large numbers of tasks emerging from dynamic environments.
In addition to computational diculties, scalability issues also arise when dealing
with multi-agent applications, in which agents require coordination and communication
to satisfy mission requirements. This dissertation is an eort towards addressing
the computational and scalability challenges of designing controllers from highlevel
requirements by employing reactive synthesis, a formal methods approach, and combining it with other decision-making processes that handle coordination among
agents to alleviate the load on reactive synthesis. The proposed framework results
in a more scalable solution with lower computational costs while guaranteeing that
high-level requirements are met. The practicality of the proposed framework is
demonstrated through various types of multi-agent applications including reghting,
re monitoring, rescue, search & rescue and ship protection scenarios.
Our approach incorporates methodology from computer science and control,
including reactive synthesis of discrete systems, metareasoning, reachability analysis
and inverse reinforcement learning. This thesis consists of two key parts: reactive
synthesis from linear temporal logic specications and specication inference
from demonstrations of formal behavior. First, we introduce the reactive synthesis
problem for which the desired system behavior species the method by which
a multi-agent system solves the problem of decentralized task allocation depending
on communication availability conditions. Second, we present the synthesis problem
formulated to obtain a high-level mission planner and controller for managing a
team of agents ghting a wildre. Third, we present a framework for inferring linear
temporal logic specications that succinctly convey and explain the observed behavior.
The gained knowledge is leveraged to improve motion prediction for agents
behaving according to the learned specication. The eectiveness of the inference
process and motion prediction framework are demonstrated through a scenario in
which humans practice social norms commonly seen in pedestrian settings.
2021-01-01T00:00:00ZSelected Problems in Many-Revolution Trajectory Optimization Using Q-LawShannon, Jacksonhttp://hdl.handle.net/1903/277662021-09-16T07:47:21Z2021-01-01T00:00:00ZSelected Problems in Many-Revolution Trajectory Optimization Using Q-Law
Shannon, Jackson
Q-Law is a Lyapunov guidance law for low-thrust trajectory design. Most prior implementations of Q-Law were limited to relatively simple low-thrust transfers. This work aims to improve the optimality, usability, and efficiency of Q-Law for better application to the mission design process. To accomplish this, Q-Law is combined with direct collocation to form an efficient hybrid method for high-fidelity, many-revolution trajectory design. Additionally, forward and backward Q-Law propagation are combined to form a novel method for Lunar transfer trajectories. This technique rapidly produces spiral trajectories to the Moon and provides mission designers with a means for efficient trade space exploration. Additionally, backward propagated Q-Law is combined with heritage trajectory design software to produce spiral escape trajectories as well as single and double Lunar swingby trajectories for interplanetary rideshare mission scenarios. Lastly, analytical partial derivatives of the Q-Law thrust vector calculation are derived, and the Q-Law algorithm is wrapped in a nonlinear programming problem. When these derivatives are used to generate the trajectory state transition matrix, the efficiency and accuracy of the optimization is superior to finite difference solutions. Using this approach, a novel Q-Law multiple shooting method is formulated and tested on various low-thrust transfer problems. These enhancements to the standard Q-Law algorithm enable efficient trade space exploration for more complex low-thrust trajectories, with a specific emphasis on the needs of SmallSat rideshare missions.
2021-01-01T00:00:00ZCylinder-Airfoil Interactions and the Effect on Airfoil PerformanceLefebvre, Jonathanhttp://hdl.handle.net/1903/277482021-09-16T07:46:31Z2021-01-01T00:00:00ZCylinder-Airfoil Interactions and the Effect on Airfoil Performance
Lefebvre, Jonathan
From micro air vehicles flying in the wake of buildings to aircraft operating in ship airwakes, turbulent flows generate unsteady aerodynamic loads on airfoils that may promote structural failure, loss of flight control, and produce noise radiation. In order to develop engineering solutions capable of mitigating these effects, accurate force prediction of airfoils encountering turbulent wakes is necessary. A barrier to such force prediction techniques is the lack of a fundamental understanding of the aerodynamics of wake-airfoil interactions. The goal of this work is to investigate the cylinder-airfoil configuration by quantifying the effect of cylinder wake turbulence on airfoil force production and identifying the underlying flow physics. Results were obtained from both wind tunnel experiments and numerical simulations using a NASA OVERFLOW solver. Four cylinder-airfoil configuration parameters were evaluated: the gap G/D and offset z/D distances between the cylinder and airfoil, the cylinder-diameter-to-airfoil-chord ratio D/c, and the cylinder cross-sectional geometry. During the investigation of each parameter, the airfoil angle of attack varied from α= -5 to 40 while the Reynolds number based on the airfoil chord c was fixed at Rec =1×10^5. Flow characterization of the region between the cylinder and airfoil revealed that the airfoil encounters a highly unsteady inflow. Turbulence intensity reaches 55% of the freestream velocity upstream of the airfoil's leading edge while the flow oscillates at the cylinder vortex shedding frequency. The influence of the upstream cylinder wake on airfoil performance was quantified by time-averaged force measurements and showed three modifications compared to a clean inflow: (1) lift augmentation, (2) negative drag or thrust, and (3) delay in stall. The unsteady airfoil behavior was also investigated, showing that the amplitude of unsteady airloads increases for small gap and offset distances, while the airfoil frequency response matches the cylinder vortex shedding frequency. Flowfield measurements show that the cylinder-airfoil interaction induces flow separation at the leading edge of the airfoil, generating a leading edge vortex (LEV). The LEV is identified as the main flow structure responsible for modifying airfoil performance as it provides lift enhancement and delays stall at large angles of attack, while at low angles of attack the LEV promotes reverse flow at the surface, contributing to negative drag. The results and analysis from this work advance the fundamental flow physics of the cylinder-airfoil interactions by revealing key flow structures responsible for the unsteady force production on an airfoil in the wake of a cylinder.
2021-01-01T00:00:00ZGlobal Nonlinear Modeling Using Automated Local Model Networks in Real TimeWeinstein, Rosehttp://hdl.handle.net/1903/277362021-09-16T07:47:15Z2021-01-01T00:00:00ZGlobal Nonlinear Modeling Using Automated Local Model Networks in Real Time
Weinstein, Rose
Global nonlinear modeling is a challenging task that spans multiple disciplines. When it is necessary to develop a model across the global input space, and a single linear model is insufficient, nonlinear modeling methods are required. If the model is constrained to be developed autonomously in real time, the modeling problem is more difficult, and there are fewer available resources, tools, and techniques for efficient and effective model development. This scenario specifically arises in the context of the NASA Learn-to-Fly concept, which aims to develop tools for real-time aerodynamic modeling and control for new or modified flight vehicles, and which serves as the motivation for this research. This work aims to develop a modeling method that enables the model to be developed automatically in real time, with limited prior knowledge required, and that provides a model that is easily interpretable, allows physical insight into the system, and offers good global and local prediction capabilities. A novel method is developed and presented in this work for automated real-time global nonlinear modeling using local model networks, known as Smoothed Partitioning with LocalIzed Trees in Real time (SPLITR). The global nonlinear system behavior is partitioned into several local regions known as cells, with the dimension, location, and timing of each partition automatically selected based on a new residual characterization procedure, under the constraints of real-time operation. Regression trees represent the successive partitioning of the global input space and describe the evolution of the cell structure. Recursive equation-error least-squares parameter estimation in the time domain is used to estimate a model that represents the local system behavior in each region so that the model can be updated independently with data in the explanatory variable ranges of each cell, even if the data are not contiguous in time. A weighted superposition of these piecewise local models across the input space forms a global nonlinear model that also accurately captures the local behavior. The SPLITR approach was tested and validated using both simplified simulated test data, as well as experimental flight test data, and the results were analyzed in terms of model predictive capabilities and interpretability. The results show that SPLITR can be used to automatically partition complex nonlinear behavior in real time, produce an accurate model, and provide valuable physical insight into the local and global system behavior.
2021-01-01T00:00:00Z