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At least 883 records · Page 49

On discrete control of nonlinear systems with applications to robotics

Much progress has been reported in the areas of modeling and control of nonlinear dynamic systems in a continuous-time framework. From implementation point of view, however, it is essential to study these nonlinear systems directly in a discrete setting that is amenable for interfacing with digital computers. But to develop discrete models and discrete controllers for a nonlinear system such as robot is a nontrivial task. Robot is also inherently a variable-inertia dynamic system involving additional complications. Not only the computer-oriented models of these systems must satisfy the usual requirements for such models, but these must also be compatible with the inherent capabilities of computers and must preserve the fundamental physical characteristics of continuous-time systems such as the conservation of energy and/or momentum. Preliminary issues regarding discrete systems in general and discrete models of a typical industrial robot that is developed with full consideration of the principle of conservation of energy are presented. Some research on the pertinent tactile information processing is reviewed. Finally, system control methods and how to integrate these issues in order to complete the task of discrete control of a robot manipulator are also reviewed.

Eslami, Mansour↗

The dynamics of coorbital satellite systems

The dynamical behavior of N coorbital satellites moving with the same average mean motion around a primary has been studied both analytically and by numerical integrations for N from 2 to 9 satellites. Simplified dynamical equations are used to determine the different stationary configurations and their local stability against infinitesimal perturbations. The ring of equally spaced identical satellites is found to be locally unstable for N of six or less, while for N from two to eight there exists another stable compact stationary configuration with separations of 60 deg or less between adjacent satellites. For N of seven or more the equally spaced configuration becomes locally stable, and for N of nine or more it is the only stationary configuration. The motion becomes chaotic for large-amplitude perturbations. The chaotic motion fills a restricted region of phase space whose outer boundary is determined by the maximum velocity curve.

Salo, H.↗

Application of Contraction Mappings to the Control of Nonlinear Systems

The theoretical and applied aspects of successive approximation techniques are considered for the determination of controls for nonlinear dynamical systems. Particular emphasis is placed upon the methods of contraction mappings and modified contraction mappings. It is shown that application of the Pontryagin principle to the optimal nonlinear regulator problem results in necessary conditions for optimality in the form of a two point boundary value problem (TPBVP). The TPBVP is represented by an operator equation and functional analytic results on the iterative solution of operator equations are applied. The general convergence theorems are translated and applied to those operators arising from the optimal regulation of nonlinear systems. It is shown that simply structured matrices and similarity transformations may be used to facilitate the calculation of the matrix Green functions and the evaluation of the convergence criteria. A controllability theory based on the integral representation of TPBVP's, the implicit function theorem, and contraction mappings is developed for nonlinear dynamical systems. Contraction mappings are theoretically and practically applied to a nonlinear control problem with bounded input control and the Lipschitz norm is used to prove convergence for the nondifferentiable operator. A dynamic model representing community drug usage is developed and the contraction mappings method is used to study the optimal regulation of the nonlinear system.

Killingsworth, W. R., Jr.↗

Data-driven Distributed Learning of Multi-agent Systems: A Koopman Operator Approach

Koopman operator theory provides a model-free technique for studying nonlinear dynamical systems purely from data. Since the Koopman operator is infinite-dimensional, researchers have developed several methods that provide a finite-dimensional approximation of the Koopman operator so that it can be applied for practical use cases. One common thing with most of the methods is that their solutions are obtained by solving a centralized minimization problem. In this work, we treat the dynamical system to be a multi-agent system and propose an algorithm to compute the finite-dimensional approximation of the Koopman operator in a distributed manner using the knowledge of the topology of the underlying multi-agent system. The proposed distributed approach is shown to be equivalent to the centralized learning problem and results in a sparse Koopman whose block structure mimics the Laplacian of the multi-agent system. Extensive simulation studies illustrate the proposed framework on the network of oscillators and the IEEE 68 bus system.

Nandanoori, Sai Pushpak↗

Stochastic modeling and control system designs of the NASA/MSFC Ground Facility for large space structures: The maximum entropy/optimal projection approach

In the Control Systems Division of the Systems Dynamics Laboratory of the NASA/MSFC, a Ground Facility (GF), in which the dynamics and control system concepts being considered for Large Space Structures (LSS) applications can be verified, was designed and built. One of the important aspects of the GF is to design an analytical model which will be as close to experimental data as possible so that a feasible control law can be generated. Using Hyland's Maximum Entropy/Optimal Projection Approach, a procedure was developed in which the maximum entropy principle is used for stochastic modeling and the optimal projection technique is used for a reduced-order dynamic compensator design for a high-order plant.

Hsia, Wei-Shen↗

Automated identification of dominant physical processes

The identification of processes that locally and approximately dominate dynamical system behavior has enabled significant advances in understanding and modeling nonlinear differential dynamical systems. Conventional methods of dominant process identification involve piecemeal and ad hoc (non-rigorous, informal) scaling analyses to identify dominant balances of governing equation terms and to delineate the spatiotemporal boundaries (boundaries in space and/or time) of each dominant balance. For the first time, we present an objective global measure of the fit of dominant balances to observations, which is desirable for automation, and was previously undefined. Furthermore, we propose a formal definition of the dominant balance identification problem in the form of an optimization problem. Here, we show that the optimization can be performed by various machine learning algorithms, enabling the automatic identification of dominant balances. Our method is algorithm agnostic and it eliminates reliance upon expert knowledge to identify dominant balances which are not known beforehand.

42 ENGINEERING↗

NN-OpInf

SAND2026-18878O The NN-OpInf tool is a PyTorch-based approach to operator inference that uses composable, structure-preserving neural networks to represent nonlinear operators. Operator inference is a machine learning method for inferring low-dimensional systems from data and polynomial models for system dynamics. However, many systems do not conform to polynomial structures, which NN-OpInf addresses by parameterizing operators with neural networks. Sandia National Laboratories is a multimission laboratory managed and operated by National Technology & Engineering Solutions of Sandia, LLC, a wholly owned subsidiary of Honeywell International Inc., for the U.S. Department of Energy's National Nuclear Security Administration under contract DE-NA0003525.

SciDAC↗

Electron Drift Kinetics in Space and in the Lab [Slides]

In a magnetized plasma, dynamics are fundamentally split between the directions parallel and perpendicular to the magnetic field. Generally, gyration about the magnetic field will occur on smaller spatial and time scales than the bulk dynamics of the system. From Hamiltonian mechanics, a periodic motion on a time scale much shorter than the other system dynamics leads to an adiabatic invariant. The adiabatic invariant for this motion is the magnetic moment $\mu=\frac{mv⟂^2}{2B}$, which will be approximately conserved.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗

Field-Focused Load-Leveled Dynamic Wireless Charging System for Electric Vehicles

Electric vehicles offer much higher well-to-wheel efficiency than gasoline vehicles and can significantly reduce U.S. dependence on foreign oil, lower greenhouse gas emissions, improve local air quality, and support continued technological and economic growth. Despite these advantages, widespread adoption remains constrained by limitations in current battery technology, including high cost, limited driving range, and long charging times. One promising approach to address these challenges is to reduce onboard energy storage and instead deliver power wirelessly to vehicles while they are at a traffic stop or in motion.

33 ADVANCED PROPULSION SYSTEMS↗

The complex coronal transient of 1980 March 23

The time evolution of the coronal transient of 1980 March 23 is described, which contains as substructures a dark loop and an area of depletion similar to those seen by Fisher, Garcia, and Seagraves. The mass ejection pushes aside preexisting streamers, giving rise to the characteristic dark area with bright flanks seen in difference images of depletion transients. Time-height plots for five identifiable features in the transient event are shown; these substructures have velocities interrelated in a way approximately consistent with the hypothesis of self-similar MHD propagation. This implies that the event as a whole may be a self-similar dynamical system, and that the substructure loops are not independent units. A scenario for the event is presented, in which it is considered as a large, complex system which evolves dynamically as a whole.

Illing, R. M. E.↗

Integrated Analysis Capability Program

Integrated Analysis Capability (IAC) software system provides highly effective, interactive analysis tool for integrated design of large structures. Developed to serve as interface for programs from fields of structures, thermodynamics, controls, and system dynamics with executive system and data base to yield highly-efficient multidisciplinary system. Written in FORTRAN 77.

Frisch, Harold P.↗

Space Station Power System issues

A number of attractive options are available for the Space Station Power System. These include a photovoltaic system or solar dynamic system for power generation, batteries or fuel cells for energy storage and ac or dc for power management and distribution. These options are being explored during the present preliminary design and definition phase of the Space Station Program. Final selections are presently targeted for January 1986.

Forestieri, A. F.↗

Photovoltaic superiority for Space Station Freedom power in the 21st century

Complete power systems capable of delivering 75 kW of continuous power in low earth orbit have been compared. Performance, liabilities, and advantages are discussed for a shielded nuclear system, a solar dynamic system, and photovoltaic systems, both current Freedom Si design and near-term GaAs/Ge with NaS storage. System components include power generation, storage (if required), heat rejection, power conversion and distribution, structural support, and shielding (if required). Performance parameters indicate the substantial advantage of the GaAs/Ge photovoltaic system, which does not require altering the support structure of the current Freedom design.

Bailey, Sheila G.↗

New quests for better attitudes

During the past few years considerable insight was gained into the QUEST algorithm both as a maximum likelihood estimator and as a Kalman filter/smoother for systems devoid of dynamical noise. The new algorithms and software are described and analytical comparisons are made with the more conventional attitude Kalman filter. It is also described how they may be accommodated to noisy dynamical systems.

Shuster, Malcolm D.↗

Some experiences with Krylov vectors and Lanczos vectors

This paper illustrates the use of Krylov vectors and Lanczos vectors for reduced-order modeling in structural dynamics and for control of flexible structures. Krylov vectors and Lanczos vectors are defined and illustrated, and several applications that have been under study at The University of Texas at Austin are reviewed: model reduction for undamped structural dynamics systems, component mode synthesis using Krylov vectors, model reduction of damped structural dynamics systems, and one-sided and two-sided unsymmetric block-Lanczos model-reduction algorithms.

Craig, Roy R., Jr.↗

Data Augmentation for Intelligent Contingency Management Using Generative Adversarial Neural Networks

Artificial intelligence (AI)-based techniques for intelligent contingency management (ICM) require that intelligent agents learn various aspects of system dynamics to create and execute contingencies. For high assurance contingency management, agents achieve the most compelling results through supervised or semi-supervised machine learning, for which agents require large datasets to learn the dynamics of the system. Unfortunately, data collection in aerospace applications can be costly, due to both time and resources. Presented work describes a framework for data augmentation of ICM databases containing training data for machine learning models. This framework populates the database with the outputs of generative adversarial network (GAN) models that were trained on flight data. Methods for evaluating the suitability of these models based on the equations of motion, as well as other physical constraints, are discussed. The paper demonstrates the utility of this database for training intelligent agents on the NASA T2 generic transport aircraft model and experimental vertical takeoff and landing (VTOL) simulation model.

Generative Machine Learning↗

Data Augmentation for Intelligent Contingency Management Using Generative Adversarial Neural Networks

Artificial intelligence (AI)-based techniques for intelligent contingency management (ICM) require that intelligent agents learn various aspects of system dynamics to create and execute contingencies. For high assurance contingency management, agents achieve the most compelling results through supervised or semi-supervised machine learning, for which agents require large datasets to learn the dynamics of the system. Unfortunately, data collection in aerospace applications can be costly, due to both time and resources. Presented work describes a framework for data augmentation of ICM databases containing training data for machine learning models. This framework populates the database with the outputs of generative adversarial network (GAN) models that were trained on flight data. Methods for evaluating the suitability of these models based on the equations of motion, as well as other physical constraints, are discussed. The paper demonstrates the utility of this database for training intelligent agents on the NASA T2 generic transport aircraft model and experimental vertical takeoff and landing (VTOL) simulation model.

Generative Machine Learning↗

Performance and durability of high emittance heat receiver surfaces for solar dynamic power systems

Haynes 188, a cobalt-based superalloy, will be used to make thermal energy storage (TES) containment canisters for a 2 kW solar dynamic ground test demonstrator (SD GTD). Haynes 188 containment canisters with a high thermal emittance (epsilon) are desired for radiating heat away from local hot spots, improving the heating distribution, which will in turn improve canister service life. In addition to needing a high emittance, the surface needs to be durable in an elevated temperature, high vacuum environment for an extended time period. Thirty-five Haynes 188 samples were exposed to 14 different types of surface modification techniques for emittance and vacuum heat treatment (VHT) durability enhancement evaluation. Optical properties were obtained for the modified surfaces. Emittance enhanced samples were exposed to VHT for up to 2692 hours at 827 C and less than or equal to 10(exp -6) torr with integral thermal cycling. Optical properties were taken intermittently during exposure, and after final VHT exposure. The various surface modification treatments increased the emittance of pristine Haynes 188 from 0.11 up to 0.86. Seven different surface modification techniques were found to provide surfaces which met the SD GTD receiver VHT durability requirement. Of the 7 surface treatments, 2 were found to display excellent VHT durability: an alumina based (AB) coating and a zirconia based coating. The alumina based coating was chosen for the epsilon enhancement surface modification technique for the SD GTD receiver. Details of the performance and vacuum heat treatment durability of this coating and other Haynes 188 emittance surface modification techniques are discussed. Technology from this program will lead to successful demonstration of solar dynamic power for space applications, and has potential for application in other systems requiring high emittance surfaces.

Degroh, Kim K.↗