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At least 433 records · Page 24

Controlling Attitude of a Solar-Sail Spacecraft Using Vanes

A paper discusses a concept for controlling the attitude and thrust vector of a three-axis stabilized Solar Sail spacecraft using only four single degree-of-freedom articulated spar-tip vanes. The vanes, at the corners of the sail, would be turned to commanded angles about the diagonals of the square sail. Commands would be generated by an adaptive controller that would track a given trajectory while rejecting effects of such disturbance torques as those attributable to offsets between the center of pressure on the sail and the center of mass. The controller would include a standard proportional + derivative part, a feedforward part, and a dynamic component that would act like a generalized integrator. The controller would globally track reference signals, and in the presence of such control-actuator constraints as saturation and delay, the controller would utilize strategies to cancel or reduce their effects. The control scheme would be embodied in a robust, nonlinear algorithm that would allocate torques among the vanes, always finding a stable solution arbitrarily close to the global optimum solution of the control effort allocation problem. The solution would include an acceptably small angle, slow limit-cycle oscillation of the vanes, while providing overall thrust vector pointing stability and performance.

Mettler, Edward↗

Attitude Controller for the Atmospheric Entry of the Mars Science Laboratory

This paper describes the attitude controller for the atmospheric entry of the Mars Science Laboratory (MSL). The controller will command 8 RCS thrusters to control the 3- axis attitude of the entry capsule. The Entry Controller is formulated as three independent channels in the control frame, which is nominally aligned with the stability frame. Each channel has a feedfoward and a feedback path. The feedforward path enables fast response to large bank commands. The feedback path stabilizes the vehicle angle of attack and sideslip around its trim position, and tracks bank commands. The feedback path has a PD/D control structure with deadbands that minimizes fuel usage. The performance of this design is demonstrated via computer simulations.

attitude controller↗

Flight Test of an Intelligent Flight-Control System

The F-15 Advanced Controls Technology for Integrated Vehicles (ACTIVE) airplane (see figure) was the test bed for a flight test of an intelligent flight control system (IFCS). This IFCS utilizes a neural network to determine critical stability and control derivatives for a control law, the real-time gains of which are computed by an algorithm that solves the Riccati equation. These derivatives are also used to identify the parameters of a dynamic model of the airplane. The model is used in a model-following portion of the control law, in order to provide specific vehicle handling characteristics. The flight test of the IFCS marks the initiation of the Intelligent Flight Control System Advanced Concept Program (IFCS ACP), which is a collaboration between NASA and Boeing Phantom Works. The goals of the IFCS ACP are to (1) develop the concept of a flight-control system that uses neural-network technology to identify aircraft characteristics to provide optimal aircraft performance, (2) develop a self-training neural network to update estimates of aircraft properties in flight, and (3) demonstrate the aforementioned concepts on the F-15 ACTIVE airplane in flight. The activities of the initial IFCS ACP were divided into three Phases, each devoted to the attainment of a different objective. The objective of Phase I was to develop a pre-trained neural network to store and recall the wind-tunnel-based stability and control derivatives of the vehicle. The objective of Phase II was to develop a neural network that can learn how to adjust the stability and control derivatives to account for failures or modeling deficiencies. The objective of Phase III was to develop a flight control system that uses the neural network outputs as a basis for controlling the aircraft. The flight test of the IFCS was performed in stages. In the first stage, the Phase I version of the pre-trained neural network was flown in a passive mode. The neural network software was running using flight data inputs with the outputs provided to instrumentation only. The IFCS was not used to control the airplane. In another stage of the flight test, the Phase I pre-trained neural network was integrated into a Phase III version of the flight control system. The Phase I pretrained neural network provided realtime stability and control derivatives to a Phase III controller that was based on a stochastic optimal feedforward and feedback technique (SOFFT). This combined Phase I/III system was operated together with the research flight-control system (RFCS) of the F-15 ACTIVE during the flight test. The RFCS enables the pilot to switch quickly from the experimental- research flight mode back to the safe conventional mode. These initial IFCS ACP flight tests were completed in April 1999. The Phase I/III flight test milestone was to demonstrate, across a range of subsonic and supersonic flight conditions, that the pre-trained neural network could be used to supply real-time aerodynamic stability and control derivatives to the closed-loop optimal SOFFT flight controller. Additional objectives attained in the flight test included (1) flight qualification of a neural-network-based control system; (2) the use of a combined neural-network/closed-loop optimal flight-control system to obtain level-one handling qualities; and (3) demonstration, through variation of control gains, that different handling qualities can be achieved by setting new target parameters. In addition, data for the Phase-II (on-line-learning) neural network were collected, during the use of stacked-frequency- sweep excitation, for post-flight analysis. Initial analysis of these data showed the potential for future flight tests that will incorporate the real-time identification and on-line learning aspects of the IFCS.

Davidson, Ron↗

Analysis of Control Strategies for Aircraft Flight Upset Recovery

This paper proposes a framework for studying the ability of a control strategy, consisting of a control law and a command law, to recover an aircraft from ight conditions that may extend beyond the normal ight envelope. This study was carried out (i) by evaluating time responses of particular ight upsets, (ii) by evaluating local stability over an equilibrium manifold that included stall, and (iii) by bounding the set in the state space from where the vehicle can be safely own to wings-level ight. These states comprise what will be called the safely recoverable ight envelope (SRFE), which is a set containing the aircraft states from where a control strategy can safely stabilize the aircraft. By safe recovery it is implied that the tran- sient response stays between prescribed limits before converging to a steady horizontal ight. The calculation of the SRFE bounds yields the worst-case initial state corresponding to each control strategy. This information is used to compare alternative recovery strategies, determine their strengths and limitations, and identify the most e ective strategy. In regard to the control law, the authors developed feedback feedforward laws based on the gain scheduling of multivariable controllers. In regard to the command law, which is the mechanism governing the exogenous signals driving the feed- forward component of the controller, we developed laws with a feedback structure that combines local stability and transient response considera- tions. The upset recovery of the Generic Transport Model, a sub-scale twin-engine jet vehicle developed by NASA Langley Research Center, is used as a case study.

Crespo, Luis G.↗

Bi-Objective Optimal Control Modification Adaptive Control for Systems with Input Uncertainty

This paper presents a new model-reference adaptive control method based on a bi-objective optimal control formulation for systems with input uncertainty. A parallel predictor model is constructed to relate the predictor error to the estimation error of the control effectiveness matrix. In this work, we develop an optimal control modification adaptive control approach that seeks to minimize a bi-objective linear quadratic cost function of both the tracking error norm and predictor error norm simultaneously. The resulting adaptive laws for the parametric uncertainty and control effectiveness uncertainty are dependent on both the tracking error and predictor error, while the adaptive laws for the feedback gain and command feedforward gain are only dependent on the tracking error. The optimal control modification term provides robustness to the adaptive laws naturally from the optimal control framework. Simulations demonstrate the effectiveness of the proposed adaptive control approach.

Nguyen, Nhan T.↗

Control Software for Piezo Stepping Actuators

A control system has been developed for the Space Interferometer Mission (SIM) piezo stepping actuator. Piezo stepping actuators are novel because they offer extreme dynamic range (centimeter stroke with nanometer resolution) with power, thermal, mass, and volume advantages over existing motorized actuation technology. These advantages come with the added benefit of greatly reduced complexity in the support electronics. The piezo stepping actuator consists of three fully redundant sets of piezoelectric transducers (PZTs), two sets of brake PZTs, and one set of extension PZTs. These PZTs are used to grasp and move a runner attached to the optic to be moved. By proper cycling of the two brake and extension PZTs, both forward and backward moves of the runner can be achieved. Each brake can be configured for either a power-on or power-off state. For SIM, the brakes and gate of the mechanism are configured in such a manner that, at the end of the step, the actuator is in a parked or power-off state. The control software uses asynchronous sampling of an optical encoder to monitor the position of the runner. These samples are timed to coincide with the end of the previous move, which may consist of a variable number of steps. This sampling technique linearizes the device by avoiding input saturation of the actuator and makes latencies of the plant vanish. The software also estimates, in real time, the scale factor of the device and a disturbance caused by cycling of the brakes. These estimates are used to actively cancel the brake disturbance. The control system also includes feedback and feedforward elements that regulate the position of the runner to a given reference position. Convergence time for smalland medium-sized reference positions (less than 200 microns) to within 10 nanometers can be achieved in under 10 seconds. Convergence times for large moves (greater than 1 millimeter) are limited by the step rate.

Shields, Joel F.↗

Technology Needs to Support Future Mars Exploration

The Mars Program Planning Group (MPPG) under the direction of Dr. Orlando Figueroa, was chartered to develop options for a program-level architecture for robotic exploration of Mars consistent with the objective to send humans to Mars in the 2030's. Scientific pathways were defined for future exploration, and multiple architectural options were developed that meet current science goals and support the future human exploration objectives. Integral to the process was the identification of critical technologies which enable the future scientific and human exploration goals. This paper describes the process for technology capabilities identification and examines the critical capability needs identified in the MPPG process. Several critical enabling technologies that have been identified to support the robotic exploration goals and with potential feedforward application to human exploration goals. Potential roadmaps for the development and validation of these technologies are discussed, including options for subscale technology demonstrations of future human exploration technologies on robotic missions.

Mars Program Planning Group (MPPG)↗

Optimization of a Multi-Stage ATR System for Small Target Identification

An Automated Target Recognition system (ATR) was developed to locate and target small object in images and videos. The data is preprocessed and sent to a grayscale optical correlator (GOC) filter to identify possible regionsof- interest (ROIs). Next, features are extracted from ROIs based on Principal Component Analysis (PCA) and sent to neural network (NN) to be classified. The features are analyzed by the NN classifier indicating if each ROI contains the desired target or not. The ATR system was found useful in identifying small boats in open sea. However, due to "noisy background," such as weather conditions, background buildings, or water wakes, some false targets are mis-classified. Feedforward backpropagation and Radial Basis neural networks are optimized for generalization of representative features to reduce false-alarm rate. The neural networks are compared for their performance in classification accuracy, classifying time, and training time.

false alarm rate↗

A High Performance Computing Approach to Tree Cover Delineation in 1-m NAIP Imagery Using a Probabilistic Learning Framework

Tree cover delineation is a useful instrument in deriving Above Ground Biomass (AGB) density estimates from Very High Resolution (VHR) airborne imagery data. Numerous algorithms have been designed to address this problem, but most of them do not scale to these datasets, which are of the order of terabytes. In this paper, we present a semi-automated probabilistic framework for the segmentation and classification of 1-m National Agriculture Imagery Program (NAIP) for tree-cover delineation for the whole of Continental United States, using a High Performance Computing Architecture. Classification is performed using a multi-layer Feedforward Backpropagation Neural Network and segmentation is performed using a Statistical Region Merging algorithm. The results from the classification and segmentation algorithms are then consolidated into a structured prediction framework using a discriminative undirected probabilistic graphical model based on Conditional Random Field, which helps in capturing the higher order contextual dependencies between neighboring pixels. Once the final probability maps are generated, the framework is updated and re-trained by relabeling misclassified image patches. This leads to a significant improvement in the true positive rates and reduction in false positive rates. The tree cover maps were generated for the whole state of California, spanning a total of 11,095 NAIP tiles covering a total geographical area of 163,696 sq. miles. The framework produced true positive rates of around 88% for fragmented forests and 74% for urban tree cover areas, with false positive rates lower than 2% for both landscapes. Comparative studies with the National Land Cover Data (NLCD) algorithm and the LiDAR canopy height model (CHM) showed the effectiveness of our framework for generating accurate high-resolution tree-cover maps.

Segments↗

Full Gradient Solution to Adaptive Hybrid Control

This paper focuses on the adaptation mechanisms in adaptive hybrid controllers. Most adaptive hybrid controllers update two filters individually according to the filtered-reference least mean squares (FxLMS) algorithm. Because this algorithm was derived for feedforward control, it does not take into account the presence of a feedback loop in the gradient calculation. This paper provides a derivation of the proper weight vector gradient for hybrid (or feedback) controllers that takes into account the presence of feedback. In this formulation, a single weight vector is updated rather than two individually. An internal model structure is assumed for the feedback part of the controller. The full gradient is equivalent to that used in the standard FxLMS algorithm with the addition of a recursive term that is a function of the modeling error. Some simulations are provided to highlight the advantages of using the full gradient in the weight vector update rather than the approximation.

Bean, Jacob↗

Full Gradient Solution to Adaptive Hybrid Control

This paper focuses on the adaptation mechanisms in adaptive hybrid controllers. Most adaptive hybrid controllers update two filters individually according to the filtered reference least mean squares (FxLMS) algorithm. Because this algorithm was derived for feedforward control, it does not take into account the presence of a feedback loop in the gradient calculation. This paper provides a derivation of the proper weight vector gradient for hybrid (or feedback) controllers that takes into account the presence of feedback. In this formulation, a single weight vector is updated rather than two individually. An internal model structure is assumed for the feedback part of the controller. The full gradient is equivalent to that used in the standard FxLMS algorithm with the addition of a recursive term that is a function of the modeling error. Some simulations are provided to highlight the advantages of using the full gradient in the weight vector update rather than the approximation.

Bean, Jacob↗

Synergistic Use of Nighttime Satellite Data, Electric Utility Infrastructure, and Ambient Population to Improve Power Outage Detections in Urban Areas

Natural and anthropogenic hazards are frequently responsible for disaster events, leading to damaged physical infrastructure, which can result in loss of electrical power for affected locations. Remotely-sensed, nighttime satellite imagery from the Suomi National Polar-orbiting Partnership (Suomi-NPP) Visible Infrared Imaging Radiometer Suite (VIIRS) Day/Night Band (DNB) can monitor power outages in disaster-affected areas through the identification of missing city lights. When combined with locally-relevant geospatial information, these observations can be used to estimate power outages, defined as geographic locations requiring manual intervention to restore power. In this study, we produced a power outage product based on Suomi-NPP VIIRS DNB observations to estimate power outages following Hurricane Sandy in 2012. This product, combined with known power outage data and ambient population estimates, was then used to predict power outages in a layered, feedforward neural network model. We believe this is the first attempt to synergistically combine such data sources to quantitatively estimate power outages. The VIIRS DNB power outage product was able to identify initial loss of light following Hurricane Sandy, as well as the gradual restoration of electrical power. The neural network model predicted power outages with reasonable spatial accuracy, achieving Pearson coefficients (r) between 0.48 and 0.58 across all folds. Our results show promise for producing a continental United States (CONUS)- or global-scale power outage monitoring network using satellite imagery and locally-relevant geospatial data.

da/night band↗

Testing of the Lunar Reconnaissance Orbiter Attitude Control System Re-Design Without a Gyro

The Lunar Reconnaissance Orbiter (LRO) was launched in 2009 and, with itsseven science instruments, has made numerous contributions to our understandingof the moon. LRO is in an elliptical, polar lunar orbit and nominally maintainsa nadir orientation. There are frequent slews off nadir to observe various sciencetargets. LRO attitude control system (ACS) has two star trackers and a gyro forattitude estimation in an extended Kalman filter (EKF) and four reaction wheelsused in a proportional-integral-derivative (PID) controller. LRO is equipped withthrusters for orbit adjustments and momentum management. In early 2018, thegyro was powered off following a fairly rapid decline in the laser intensity on theX axis. Without the gyro, the EKF has been disabled. Attitude is provided by asingle star tracker and a coarse rate estimate is computed by a back differencingof the star tracker quaternions. Slews have also been disabled. A new rate estimationapproach makes use of a complementary filter, combining the quaterniondifferentiated rates and the integrated PID limited control torque (with reactionwheel drag and feedforward torque removed). The filtered rate estimate replacesthe MIMU rate in the EKF, resulting in minimal flight software changes. The paperwill cover the preparation and testing of the new gyroless algorithm, both inground simulations and inflight.

Halverson, Julie↗

Exponential Convergence of the Tracking Error in Adaptive Systems Without Persistent Excitation

Persistent excitation conditions which ensure parameter convergence in adaptive algorithms have been studied by many researchers. Here, conditions are investigated for exponential convergence of the tracking error in feedforward adaptive systems without persistent excitation. Particular attention is paid to the continuous-time LMS algorithm in the overparametrized case. Results are presented.

control↗

Beam-Waveguide Antenna Servo Design Issues for Tracking Low-Earth-Orbiting Satellites

The upcoming NASA missions will require tracking of low-orbit satellites. As a consequence, NASA antennas will be required to track satellites at higher rates than for the current deep space missions. This paper investigates servo design issues for the 34-m beam-waveguide (BWG) antennas that track low-orbit satellites. This includes the upgrade of the servo with the feedforward loop, monopulse controller design, and tracking error reduction through proper choice of elevation pinion location, through application of a notch filter, and through the elevation drive amplifier gain adjustment. Finally, improvement of the signal-to-noise ratio through averaging of the oversampled monopulse signal is presented.

Mellstrom, J. A.↗

Design and Analysis of Convolutional Neural Network for RF Signal Modulation Classification for In-Orbit Deployment

To effectively transmit data to and from satellites requires a complex and robust RF communication system. Commonly, several different types of signal modulations may be required to maximize satellite efficiency depending on a variety of unexpected channel impairments. We propose a neural network algorithm capable of learning these RF signal modulations using a supervised learning technique designed for low power, high-efficiency in-orbit deployment. The work presented demonstrates a convolutional neural network (CNN) capable of learning and recognizing a set of modulation schemes commonly used to transmit RF information. We are capable of recognizing the modulation scheme from the I and Q data channels directly, with no preprocessing or data conversion required other than breaking the incoming signal into a set of uniform normalized samples. We perform a network design and size analysis, showing that reasonably high accuracy can be obtained using networks with a relatively low number of trainable parameters. Given that a user of a system such as this may wish to receive a signal using a modulation scheme that the network has not previously learned, we demonstrate that transfer learning can learn new modulation schemes by retraining only the fully connected layers in the CNN. Thus, this type of network would excel in outer space deployment using high-efficiency transfer learning hardware. Modulation recognition can be performed through rapid feedforward computation, and the CNN training process is significantly simplified when learning new modulations is required.

CNN↗

Artemis Internal Science Team Update: Deployed Payloads

Artemis will reestablish human presence on the Moon and lead to a new era of scientific discovery and exploration. Led by the National Aeronautics and Space Administration (NASA), the Artemis effort includes a collaboration of space agencies and companies from around the world [1]. In support of Artemis, a cross-disciplinary effort of science, engineering, operations, and human factors personnel is currently developing the best methods, facilities, and field locations to test hardware, train astronauts, and evaluate concepts of operations. This abstract, as part of the Artemis Internal Science Team (AIST) [2], provides an update to NASA’s plans for the solicitation, development, and operations of deployed surface payloads. In calendar year 2023, NASA’s Science Mission Directorate will solicit proposals for instruments to be deployed by crew on the surface of the Moon (Fig. 1), beginning with Artemis III [3]. The Artemis III mission will land in the south polar region of the Moon, within 6º of latitude from the south pole, in the vicinity of both persistently illuminated and permanently shadowed areas of the Moon, with potential access to surface-accessible volatile deposits [4]. Several of the proposed landing regions are located among some of the oldest parts of the Moon, and together with the permanently shadowed regions, provide the opportunity to learn about the history of the Moon through previously unexplored lunar regions [5]. Deployed payloads will be a critical part of a notional program that captures the highest-priority science for Artemis III and provides the greatest feedforward to follow-on missions and the build-up to the Artemis Base Camp

R. C. Weber↗