Robust Dynamic Watermarking for Cyber-Physical Security of Inverter-Based Resources in Power Distribution Systems
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Automation & Control Systems↗
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Results for “system identification”
Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.
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Robust nonlinear control of vectored thrust aircraft
An interdisciplinary program in robust control for nonlinear systems with applications to a variety of engineering problems is outlined. Major emphasis will be placed on flight control, with both experimental and analytical studies. This program builds on recent new results in control theory for stability, stabilization, robust stability, robust performance, synthesis, and model reduction in a unified framework using Linear Fractional Transformations (LFT's), Linear Matrix Inequalities (LMI's), and the structured singular value micron. Most of these new advances have been accomplished by the Caltech controls group independently or in collaboration with researchers in other institutions. These recent results offer a new and remarkably unified framework for all aspects of robust control, but what is particularly important for this program is that they also have important implications for system identification and control of nonlinear systems. This combines well with Caltech's expertise in nonlinear control theory, both in geometric methods and methods for systems with constraints and saturations.
Doyle, John C.↗
Intelligent Control for the BEES Flyer
This paper describes the effort to provide a preliminary capability analysis and a neural network based adaptive flight control system for the JPL-led BEES aircraft project. The BEES flyer was envisioned to be a small, autonomous platform with sensing and control systems mimicking those of biological systems for the purpose of scientific exploration on the surface of Mars. The platform is physically tightly constrained by the necessity of efficient packing within rockets for the trip to Mars. Given the physical constraints, the system is not an ideal configuration for aerodynamics or stability and control. The objectives of this effort are to evaluate the aerodynamics characteristics of the existing design, to make recommendaaons as to potential improvements and to provide a control system that stabilizes the existing aircraft for nominal flight and damaged conditions. Towards this several questions are raised and analyses are presented to arrive at answers to some of the questions raised. CART3D, a high-fidelity inviscid analysis package for conceptual and preliminary aerodynamic design, was used to compute a parametric set of solutions over the expected flight domain. Stability and control derivatives were extracted from the database and integrated with the neural flight control system. The Integrated Vehicle Modeling Environment (IVME) was also used for estimating aircraft geometric, inertial, and aerodynamic characteristics. A generic neural flight control system is used to provide adaptive control without the requirement for extensive gain scheduling or explicit system identification. The neural flight control system uses reference models to specify desired handling qualities in the roll, pitch, and yaw axes, and incorporates both pre-trained and on-line learning neural networks in the inverse model portion of the controller. Results are presented for the BEES aircraft in the subsonic regime for terrestrial and Martian environments.
Krishnakumar, K.↗
Constrained Block Nonlinear Neural Dynamical Models
Neural network modules conditioned by known priors can be effectively trained and combined to represent systems with nonlinear dynamics. This work explores a novel formulation for data-efficient learning of deep control-oriented nonlinear dynamical models by embedding local model structure and constraints. The proposed method consists of neural network blocks that represent input, state, and output dynamics with constraints placed on the network weights and system variables. For handling partially observable dynamical systems, we utilize a state observer neural network to estimate the states of the system's latent dynamics. We evaluate the performance of the proposed architecture and training methods on system identification tasks for three nonlinear systems: a continuous stirred tank reactor, a two tank interacting system, and an aerodynamics body. Models optimized with a few thousand system state observations accurately represent system dynamics in open loop simulation over thousands of time steps from a single set of initial conditions. Experimental results demonstrate an order of magnitude reduction in open-loop simulation mean squared error for our constrained, block-structured neural models when compared to traditional unstructured and unconstrained neural network models.
Skomski, Elliott↗
Convergence of Weak-SINDy Surrogate Models
In this paper, we give an in-depth error analysis for surrogate models generated by a variant of the Sparse Identification of Nonlinear Dynamics (SINDy) method. We start with an overview of a variety of nonlinear system identification techniques, namely SINDy, weak-SINDy, and the occupation kernel method. Under the assumption that the dynamics are a finite linear combination of a set of basis functions, these methods establish a linear system to recover coefficients. We illuminate the structural similarities between these techniques and establish a projection property for the weak-SINDy technique. Following the overview, we analyze the error of surrogate models generated by a simplified version of weak-SINDy. In particular, under the assumption of boundedness of a composition operator given by the solution, we show that (i) the surrogate dynamics converges towards the true dynamics and (ii) the solution of the surrogate model is reasonably close to the true solution. Finally, as an application, we discuss the use of a combination of weak-SINDy surrogate modeling and proper orthogonal decomposition (POD) to build a surrogate model for partial differential equations (PDEs).
97 MATHEMATICS AND COMPUTING↗
Methods, Systems and Apparatuses for Radio Frequency Identification
A system for radio frequency identification (RFID) includes an enclosure defining an interior region interior to the enclosure, and a feed for generating an electromagnetic field in the interior region in response to a signal received from an RFID reader via a radio frequency (RF) transmission line and, in response to the electromagnetic field, receiving a signal from an RFID sensor attached to an item in the interior region. The structure of the enclosure may be conductive and may include a metamaterial portion, an electromagnetically absorbing portion, or a wall extending in the interior region. Related apparatuses and methods for performing RFID are provided.
Fink, Patrick W.↗
Methods, Systems and Apparatuses for Radio Frequency Identification
A system for radio frequency identification (RFID) includes an enclosure defining an interior region interior to the enclosure, and a feed for generating an electromagnetic field in the interior region in response to a signal received from an RFID reader via a radio frequency (RF) transmission line and, in response to the electromagnetic field, receiving a signal from an RFID sensor attached to an item in the interior region. The structure of the enclosure may be conductive and may include a metamaterial portion, an electromagnetically absorbing portion, or a wall extending in the interior region. Related apparatuses and methods for performing RFID are provided.
Fink, Patrick W.↗
Methods, Systems and Apparatuses for Radio Frequency Identification
A system for radio frequency identification (RFID) includes an enclosure defining an interior region interior to the enclosure, and a feed for generating an electromagnetic field in the interior region in response to a signal received from an RFID reader via a radio frequency (RF) transmission line and, in response to the electromagnetic field, receiving a signal from an RFID sensor attached to an item in the interior region. The structure of the enclosure may be conductive and may include a metamaterial portion, an electromagnetically absorbing portion, or a wall extending in the interior region. Related apparatuses and methods for performing RFID are provided.
Fink, Patrick W.↗
Identification of linearized equations of motion for the fixed wing configuration of the rotor systems research aircraft
The purpose of this report is to establish linear, decoupled models of rigid body motion for the fixed wing configuration of the Rotor Systems Research Aircraft (RSRA). Longitudinal and lateral control surface fixed linear models were created from aircraft time histories using current system identification techniques. Models were obtained from computer simulation at 160 KCAS and 200 KCAS, and from flight data at 160 KCAS. Comparisons were performed to examine modeling accuracy, variation of dynamics with airspeed and correlation of simulation and flight data results. The results showed that the longitudinal and lateral linear models accurately predicted RSRA dynamics. The flight data results showed that no significant handling qualities problems were present in the RSRA fixed wing aircraft at the flight speed tested.
Balough, D. L.↗
Parameter identification and state estimation for linear systems
Parameter identification and state estimation for linear systems
Budin, M. A.↗
Next Generation Safeguards Initiative: Overview and Policy Context of UF6 Cylinder Tracking Program
Thousands of cylinders containing uranium hexafluoride (UF{sub 6}) move around the world from conversion plants to enrichment plants to fuel fabrication plants, and their contents could be very useful to a country intent on diverting uranium for clandestine use. Each of these large cylinders can contain close to a significant quantity of natural uranium (48Y cylinder) or low-enriched uranium (LEU) (30B cylinder) defined as 75 kg {sup 235}U which can be further clandestinely enriched to produce 1.5 to 2 significant quantities of high enriched uranium (HEU) within weeks or months depending on the scale of the clandestine facility. The National Nuclear Security Administration (NNSA) Next Generation Safeguards Initiative (NGSI) kicked off a 5-year plan in April 2011 to investigate the concept of a unique identification system for UF{sub 6} cylinders and potentially to develop a cylinder tracking system that could be used by facility operators and the International Atomic Energy Agency (IAEA). The goal is to design an integrated solution beneficial to both industry and inspectorates that would improve cylinder operations at the facilities and provide enhanced capabilities to deter and detect both diversion of low-enriched uranium and undeclared enriched uranium production. The 5-year plan consists of six separate incremental tasks: (1) define the problem and establish the requirements for a unique identification (UID) and monitoring system; (2) develop a concept of operations for the identification and monitoring system; (3) determine cylinder monitoring devices and technology; (4) develop a registry database to support proof-of-concept demonstration; (5) integrate that system for the demonstration; and (6) demonstrate proof-of-concept. Throughout NNSA's performance of the tasks outlined in this program, the multi-laboratory team emphasizes that extensive engagement with industry stakeholders, regulatory authorities and inspectorates is essential to its success.
11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
Integrated identification and robust control tuning for large space structures
System identification is studied for the explicit purpose of supporting modern H-infinity robust control design objectives. In the analysis, the true plant is not assumed to be in the identification model set. An integrated identification/robust control problem is posed in which the optimal solution guarantees the best robust performance relative to the system information contained in a given experimental data set. A numerical example demonstrating an approximate solution to the problem indicates the usefulness of the approach.
Yam, Y.↗
SympGNNs: Symplectic Graph Neural Networks for identifying high-dimensional Hamiltonian systems and node classification
Existing neural network models to learn Hamiltonian systems, such as SympNets, although accurate in low-dimensions, struggle to learn the correct dynamics for high-dimensional many-body systems. Herein, we introduce Symplectic Graph Neural Networks (SympGNNs) that can effectively handle system identification in high-dimensional Hamiltonian systems, as well as node classification. SympGNNs combine symplectic maps with permutation equivariance, a property of graph neural networks. Specifically, we propose two variants of SympGNNs: (i) G-SympGNN and (ii) LA-SympGNN, arising from different parameterizations of the kinetic and potential energy. We demonstrate the capabilities of SympGNN on two physical examples: a 40-particle coupled Harmonic oscillator, and a 2000-particle molecular dynamics simulation in a two-dimensional Lennard-Jones potential. Furthermore, we demonstrate the performance of SympGNN in the node classification task, achieving accuracy comparable to the state-of-the-art. Finally, we also empirically show that SympGNN can overcome the oversmoothing and heterophily problems, two key challenges in the field of graph neural networks.
Deep learning↗
Indirect synthesis of multi-degree of freedom transient systems
An indirect synthesis method is used in the efficient optimal design of multi-degree of freedom, multi-design element, nonlinear, transient systems. A limiting performance analysis which requires linear programming for a kinematically linear system is presented. The system is selected using system identification methods such that the designed system responds as closely as possible to the limiting performance. The efficiency is a result of the method avoiding the repetitive systems analyses accompanying other numerical optimization methods.
Pilkey, W. D.↗
Proceedings of the 9th Annual Conference on Manual Control
Papers are reported which were presented at the conference in the areas of displays, ride qualities and handling, driving and psychomotor skills, control, system identification and signal detection, electrophysiological and systems analysis, and modelling.
Source record↗
Multishaker modal testing
A component mode synthesis method for damped structures was developed and modal test methods were explored which could be employed to determine the relevant parameters required by the component mode synthesis method. Research was conducted on the following topics: (1) Development of a generalized time-domain component mode synthesis technique for damped systems; (2) Development of a frequency-domain component mode synthesis method for damped systems; and (3) Development of a system identification algorithm applicable to general damped systems. Abstracts are presented of the major publications which have been previously issued on these topics.
Craig, R. R., Jr.↗
Linear and nonlinear ARMA model parameter estimation using an artificial neural network
This paper addresses parametric system identification of linear and nonlinear dynamic systems by analysis of the input and output signals. Specifically, we investigate the relationship between estimation of the system using a feedforward neural network model and estimation of the system by use of linear and nonlinear autoregressive moving-average (ARMA) models. By utilizing a neural network model incorporating a polynomial activation function, we show the equivalence of the artificial neural network to the linear and nonlinear ARMA models. We compare the parameterization of the estimated system using the neural network and ARMA approaches by utilizing data generated by means of computer simulations. Specifically, we show that the parameters of a simulated ARMA system can be obtained from the neural network analysis of the simulated data or by conventional least squares ARMA analysis. The feasibility of applying neural networks with polynomial activation functions to the analysis of experimental data is explored by application to measurements of heart rate (HR) and instantaneous lung volume (ILV) fluctuations.
NASA Discipline Regulatory Physiology↗
Wind turbine blade fatigue tests: lessons learned and application to SHM system development
This paper presents experimental results of several structural health monitoring (SHM) methods applied to a 9-meter CX-100 wind turbine blade that underwent fatigue loading. The blade was instrumented with piezoelectric transducers, accelerometers, acoustic emission sensors, and foil strain gauges. It underwent harmonic excitation at its first natural frequency using a hydraulically actuated resonant excitation system. The blade was initially excited at 25% of its design load, and then with steadily increasing loads until it failed. Various data were collected between and during fatigue loading sessions. The data were measured over multiple frequency ranges using a variety of acquisition equipment, including off-the-shelf systems and specially designed hardware developed by the authors. Modal response, diffuse wave-field transfer functions, and ultrasonic guided wave methods were applied to assess the condition of the wind turbine blade. The piezoelectric sensors themselves were also monitored using a sensor diagnostics procedure. This paper summarizes experimental procedures and results, focusing particularly on fatigue crack detection, and concludes with considerations for implementing such damage identification systems, which will be used as a guideline for future SHM system development for operating wind turbine blades.
17 WIND ENERGY↗