Engineering topics

Rothhaar, Paul

Publications and source records attributed to Rothhaar, Paul.

6 records

NASA NTRS2019

Suppression of Nonlinear Rotary Slosh Dynamics Using the SLS Adaptive Augmenting Control System Demonstration on a Quadcopter Testbed

Liquid propellant makes up a significant portion of the total weight for large launch vehicles such as Saturn V, Space Shuttle, and the Space Launch System. Careful attention must be given to the influence of fuel slosh motion on the stability of the vehicle in the design of the Flight Control System (FCS). Historically, there have been instances where a poorly designed FCS in addition to a lack of passive damping have caused the slosh mass to drive the launch vehicle unstable. The dynamics behind controlling a quadcopter/hanging mass configuration is analogous to that of controlling the attitude of a rocket with a single propellant tank. The quadcopter/hanging mass configuration offers a reasonably accurate platform for assessing the real-time effectiveness of the SLS Adaptive Augmenting Controller in suppressing slosh instability. Flight test experiments were carried out at the NASA Langley Research Center's Autonomy Incubator. During both simulation and flight test, the hanging mass was intentionally made unstable and the adaptive algorithm successfully suppressed the instability as expected.

Pei, Jing↗

NASA NTRS2018

Demonstration of the Space Launch System Augmenting Adaptive Control Algorithm on Pole-Cart Platform

NASA's baseline Space Launch System (SLS) ight control system (FCS) includes an adaptive augmenting control (AAC) portion in addition to the ight-heritage nominal classical controller. The AAC algorithm is intended to improve the robustness and performance of the classical controller. Over the past several years, the AAC algorithm developed at NASA Marshall Space Flight Center (MSFC) has matured significantly through extensive simulation, rigorous analytical proofs, and a series of successful ight tests on a F18 aircraft. This study was part of a SLS program and NASA Engineering and Safety Center (NESC) joint e ort to further increase the confidence level of the AAC algorithm by demonstrating its key functionalities on a classroom type of example, the pole-cart sys- tem, at the NASA Langley Research Center (LaRC) dynamics and control laboratory. The fundamental dynamics behind balancing an inverted pendulum is similar to controlling an aerodynamically unstable rocket. Both systems are inherently open-loop unstable and requires feedback control for attitude stabilization. The principles behind the AAC algorithm is applicable to a wide range of conditionally stable dynamical systems. Hence, the outcomes from this simple and inexpensive exercise has provided the SLS program with additional confidence into the AAC design, operation, robustness, and application.

Pei, Jing↗

NASA NTRS2016

Using Multimodal Input for Autonomous Decision Making for Unmanned Systems

Autonomous decision making in the presence of uncertainly is a deeply studied problem space particularly in the area of autonomous systems operations for land, air, sea, and space vehicles. Various techniques ranging from single algorithm solutions to complex ensemble classifier systems have been utilized in a research context in solving mission critical flight decisions. Realized systems on actual autonomous hardware, however, is a difficult systems integration problem, constituting a majority of applied robotics development timelines. The ability to reliably and repeatedly classify objects during a vehicles mission execution is vital for the vehicle to mitigate both static and dynamic environmental concerns such that the mission may be completed successfully and have the vehicle operate and return safely. In this paper, the Autonomy Incubator proposes and discusses an ensemble learning and recognition system planned for our autonomous framework, AEON, in selected domains, which fuse decision criteria, using prior experience on both the individual classifier layer and the ensemble layer to mitigate environmental uncertainty during operation.

Neilan, James H.↗

NASA NTRS2015

Demonstration of Launch Vehicle Slosh Instability on Pole-Cart Platform

Liquid propellant makes up a significant portion of the total weight for large launch vehicles such as Saturn V, Space Shuttle, and the Space Launch System (SLS). Careful attention must be given to the influence of fuel slosh motion on the stability of the vehicle. A well-documented slosh danger zone occurs when the slosh mass is between the vehicle center of mass and the center of percussion. Passive damping via slosh baffle is generally required when the slosh mass is within this region. The pole-cart hardware system, typically used for academic purposes, has similar dynamic characteristics as an unstable launch vehicle. This setup offers a simple and inexpensive way of analyzing slosh dynamics and its impact on flight control design. In this paper, experimental and numerical results from the pole-cart system will be shown and direct analogies to launch vehicle slosh dynamics will be made.

Pei, Jing↗

NASA NTRS2015

Operating in "Strange New Worlds" and Measuring Success - Test and Evaluation in Complex Environments

Software tools are being developed by the Autonomy Incubator at NASA's Langley Research Center that will provide an integrated and scalable capability to support research and non-research flight operations across several flight domains, including urban and mixed indoor-outdoor operations. These tools incorporate a full range of data products to support mission planning, approval, flight operations, and post-flight review. The system can support a number of different operational scenarios that can incorporate live and archived data streams for UAS operators, airspace regulators, and other important stakeholders. Example use cases are described that illustrate how the tools will benefit a variety of users in nominal and off-nominal operational scenarios. An overview is presented for the current state of the toolset, including a summary of current demonstrations that have been completed. Details of the final, fully operational capability are also presented, including the interfaces that will be supported to ensure compliance with existing and future airspace operations environments.

Qualls, Garry↗

NASA NTRS2015

Reinforcement Learning with Autonomous Small Unmanned Aerial Vehicles in Cluttered Environments

We present ongoing work in the Autonomy Incubator at NASA Langley Research Center (LaRC) exploring the efficacy of a data set aggregation approach to reinforcement learning for small unmanned aerial vehicle (sUAV) flight in dense and cluttered environments with reactive obstacle avoidance. The goal is to learn an autonomous flight model using training experiences from a human piloting a sUAV around static obstacles. The training approach uses video data from a forward-facing camera that records the human pilot's flight. Various computer vision based features are extracted from the video relating to edge and gradient information. The recorded human-controlled inputs are used to train an autonomous control model that correlates the extracted feature vector to a yaw command. As part of the reinforcement learning approach, the autonomous control model is iteratively updated with feedback from a human agent who corrects undesired model output. This data driven approach to autonomous obstacle avoidance is explored for simulated forest environments furthering autonomous flight under the tree canopy research. This enables flight in previously inaccessible environments which are of interest to NASA researchers in Earth and Atmospheric sciences.

Tran, Loc↗