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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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All sourcesNASA NTRSDOE OSTI
DOE OSTI2011

Quantum optimal control theory in the linear response formalism

Quantum optimal control theory (QOCT) aims at finding an external field that drives a quantum system in such a way that optimally achieves some predefined target. In practice, this normally means optimizing the value of some observable, a so-called merit function. In consequence, a key part of the theory is a set of equations, which provides the gradient of the merit function with respect to parameters that control the shape of the driving field. We show that these equations can be straightforwardly derived using the standard linear response theory, only requiring a minor generalization: the unperturbed Hamiltonian is allowed to be time dependent. As a result, the aforementioned gradients are identified with certain response functions. This identification leads to a natural reformulation of QOCT in terms of the Keldysh contour formalism of the quantum many-body theory. In particular, the gradients of the merit function can be calculated using the diagrammatic technique for nonequilibrium Green's functions, which should be helpful in the application of QOCT to computationally difficult many-electron problems.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2025

Control simulations of many-body quantum systems by a synergism of discrete real-time learning and optimal control theory

We present a self-consistent algorithm for optimal control simulations of many-body quantum systems. The algorithm features a two-step synergism that combines discrete real-time machine learning (DRTL) with Quantum Optimal Control Theory (QOCT) using the time-dependent Schrödinger equation. Specifically, in step (1), DRTL is employed to identify a compact working space (i.e., the important portion of the Hilbert space) for the time evolution of the many-body quantum system in the presence of a control field (i.e., the initial or previously updated field), and in step (2), QOCT utilizes the DRTL-determined working space to find a newly updated control field for a chosen objective. Steps 1 and 2 are iterated until a self-consistent control objective value is reached such that the resulting optimal control field yields the same targeted objective value when the corresponding working space is systematically enlarged. Furthermore, to demonstrate this two-step self-consistent DRTL-QOCT synergistic algorithm, we perform optimal control simulations of strongly interacting 1D as well as 2D Heisenberg spin systems. In both scenarios, only a single spin (at the left end site for 1D and the upper left corner site for 2D) is driven by the time-dependent control fields to create an excitation at the opposite site as the target. It is found that, starting from all spin-down zero excitation states, the synergistic method is able to identify working spaces and convergence of the desired controlled dynamics with just a few iterations of the overall algorithm. In the cases studied, the dimensionality of the working space scales only quasi-linearly with the number of spins.

Artificial neural networks↗
DOE OSTI2025

Achieving designed texture and flows in bulk active nematics using optimal control theory

Being intrinsically nonequilibrium, active materials can potentially perform functions that would be thermodynamically forbidden in passive materials. However, active systems have diverse local attractors that correspond to distinct dynamical states, many of which exhibit chaotic turbulent-like dynamics and thus cannot perform work or useful functions. Designing such a system to choose a specific dynamical state is a formidable challenge. Motivated by recent advances enabling optogenetic control of experimental active materials, we describe an optimal control theory framework that identifies a spatiotemporal sequence of light-generated activity that drives an active nematic system toward a prescribed dynamical steady state. Active nematics are unstable to spontaneous defect proliferation and chaotic streaming dynamics in the absence of control. We demonstrate that optimal control theory can compute activity fields that redirect the dynamics into a variety of alternative dynamical programs and functions. This includes dynamically reconfiguring between states, selecting and stabilizing emergent behaviors that do not correspond to attractors, and are hence unstable in the uncontrolled system. Furthermore, our results provide a roadmap to leverage optical control methods to rationally design structure, dynamics, and function in a wide variety of active materials.

Complex systems theory↗
DOE OSTI2012

Optimal control of open quantum systems: A combined surrogate Hamiltonian optimal control theory approach applied to photochemistry on surfaces

In this paper, control of open quantum systems with emphasis on the control of surface photochemical reactions is presented. A quantum system in a condensed phase undergoes strong dissipative processes. From a theoretical viewpoint, it is important to model such processes in a rigorous way. In this work, the description of open quantum systems is realized within the surrogate Hamiltonian approach [R. Baer and R. Kosloff, J. Chem. Phys. 106, 8862 (1997)]. An efficient and accurate method to find control fields is optimal control theory (OCT) [W. Zhu, J. Botina, and H. Rabitz, J. Chem. Phys. 108, 1953 (1998); Y. Ohtsuki, G. Turinici, and H. Rabitz, J. Chem. Phys. 120, 5509 (2004)]. To gain control of open quantum systems, the surrogate Hamiltonian approach and OCT, with time-dependent targets, are combined. Three open quantum systems are investigated by the combined method, a harmonic oscillator immersed in an ohmic bath, CO adsorbed on a platinum surface, and NO adsorbed on a nickel oxide surface. Throughout this paper, atomic units, i.e., ({Dirac_h}/2{pi})=m{sub e}=e=a{sub 0}= 1, have been used unless otherwise stated.

36 MATERIALS SCIENCE↗
DOE OSTI2011

Local control theory in trajectory-based nonadiabatic dynamics

In this paper, we extend the implementation of nonadiabatic molecular dynamics within the framework of time-dependent density-functional theory in an external field described in Tavernelli et al.[Phys. Rev. A 81, 052508 (2010)] by calculating on-the-fly pulses to control the population transfer between electronic states using local control theory. Using Tully's fewest switches trajectory surface hopping method, we perform MD to control the photoexcitation of LiF and compare the results to quantum dynamics (QD) calculations performed within the Heidelberg multiconfiguration time-dependent Hartree package. We show that this approach is able to calculate a field that controls the population transfer between electronic states. The calculated field is in good agreement with that obtained from QD, and the differences that arise are discussed in detail.

74 ATOMIC AND MOLECULAR PHYSICS↗
DOE OSTI2012

Phase-only shaped laser pulses in optimal control theory: Application to indirect photofragmentation dynamics in the weak-field limit

We implement phase-only shaped laser pulses within quantum optimal control theory for laser-molecule interaction. This approach is applied to the indirect photofragmentation dynamics of NaI in the weak-field limit. It is shown that optimized phase-modulated pulses with a fixed frequency distribution can substantially modify transient dissociation probabilities as well as the momentum distribution associated with the relative motion of Na and I.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2020

Modeling and Control Using Stochastic Distribution Control Theory for Intersection Traffic Flow

In this work, we investigated stochastic distribution control theory-based traffic signal optimization to achieve a smooth and uniform flow of vehicles through signalized intersections. In this context, the static and linear dynamic stochastic distribution models were developed to express the relationship between the signal timing and the traffic queue length together with its probability density function. Two stochastic distribution control algorithms were designed to control the signal timing at intersections such that the probability density function of the traffic queue of each intersection road segment is made as narrow and as small as possible. Also, a recursive input-output traffic queue estimation model was proposed, which is data-driven and dynamic in nature, to calculate real-time traffic queue length using traffic signal timings and loop-detector data. The control algorithms were evaluated for a one-signal corridor, two-signal corridor, and 2 x 2 network of signalized intersections. MATLAB simulation examples are provided to demonstrate the use of the proposed algorithms and comparison to the existing widely-used semi-actuated control has been made. Desired results were obtained.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2012

Convex hulls of a curve in control theory

A classification is obtained for typical singularities of the local transitivity sets of control systems on three-dimensional manifolds with nonconvex indicatrices that are closed smooth spatial curves. Bibliography: 8 titles.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2010

Quantum control theory for state transformations: Dark states and their enlightenment

For many quantum information protocols such as state transfer, entanglement transfer, and entanglement generation, standard notions of controllability for quantum systems are too strong. We introduce the weaker notion of accessible pairs and prove an upper bound on the achievable fidelity of a transformation between a pair of states based on the symmetries of the system. A large class of spin networks is presented for which this bound can be saturated. In this context, we show how the inaccessible dark states for a given excitation-preserving evolution can be calculated and illustrate how some of these can be accessed using extra catalytic excitations. This emphasizes that it is not sufficient for analyses of state transfer in spin networks to restrict to the single excitation subspace. One class of symmetries in these spin networks is exactly characterized in terms of the underlying graph properties.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2021

Survey on stochastic distribution systems: A full probability density function control theory with potential applications

Complex systems seen either in general engineering practice or economics are subjected to ever increased uncertainties that are mostly represented as random variables or parameters, and the characteristics of random variables are represented by their probability density functions (PDFs). Controlling their PDFs means to shape their stochastic distributions and in general it would provide a full treatment for system analysis and operational control and optimization. This leads to the development of stochastic distribution control (SDC) systems theory in the past decades, where the original aim of the controller design is to realize a shape control of the distributions of certain random variables in their PDFs sense for some engineering processes. Indeed, once the PDFs of these random variables or parameters are used to describe their distribution characters, the control task is to obtain control signals so that the output PDFs of stochastic systems are made to follow their target PDFs. The subject of SDC was initially originated for non-Gaussian stochastic control systems design but has found a wide spectrum of applications in general systems in terms of data-driven modeling, analysis, signal processing (filtering), data mining via multivariable statistics, decision-making (optimization) for systems subjected to uncertainties and even in economics. In this context, SDC constitutes an effective primer tool for complex system analysis, control and operational optimizations. In this review paper, a detailed survey of the developments on the research of SDC systems will be made together with their wide spectrum applications and future perspectives.

42 ENGINEERING↗
DOE OSTI2016

Application of Optimal Production Control theory for Home Energy Management in a Micro Grid

We consider the optimal stochastic control problem for home energy systems with solar and energy storage devices when the demand is realized from the grid. The demand is subject to Brownian motions with both drift and variance parameters modulated by a continuous-time Markov chain that represents the regime of electricity price. We model the systems as pure stochastic differential equation models, and then we follow the completing square technique to solve the stochastic home energy management problem. The effectiveness of the efficiency of the proposed approach is validated through a simulation example. For practical situations with constraints consistent to those studied here, our results imply the proposed framework could reduce the electricity cost from short-term purchase in peak hour market.

Malikopoulos, Andreas↗
DOE OSTI2011

Design And Verification of Controllers for Coupled Bunch Instabilities Using Optimal Control Theory And Numerical Simulation: Predictions for PEP II

We present a technique for the design and verification of efficient bunch-by-bunch controllers for damping longitudinal multibunch instabilities. The controllers attempt to optimize the use of available feedback amplifier power - one of the most expensive components of a feedback system - and define the limits of the closed loop system performance. Our design technique alternates between analytic computation of single bunch optimal controllers and verification on a multibunch numerical simulator. The simulator uses PEP-II parameters and identifies unstable coupled bunch modes, their growth rates and their damping rates with feedback. The results from the simulator are shown to be in reasonable agreement with analytical calculations based on the single bunch model. The technique is then used to evaluate the performance of a variety of controllers proposed for PEP-II.

43 PARTICLE ACCELERATORS↗
DOE OSTI2023

Data-Driven Control: Theory and Applications

The ushering in of the big-data era, ably supported by exponential advances in computation, has provided new impetus to data-driven control in several engineering sectors. This topic's rapid and deep expansion has precipitated the need to showcase the highlights of data-driven approaches. There has been a rich history of contributions from the control systems community in data-driven control. At the same time, several new concepts and research directions have also been introduced in recent years. Many of these contributions and concepts have started to transition from theory to practical applications. This paper will overview the historical contributions and highlight recent concepts and research directions.

Soudbakhsh, Damoon↗
DOE OSTI2011

Optimization and Control Theory for Smart (Power) Grids [Slides]

The national interest in a broad class of problems loosely characterized as the smart grid is rapidly growing. At its core, the smart grid is the modernization of our existing electric power systems through improvements in efficiency, increased robustness and resiliency, integration of time-variable renewable generation, and reduction of environmental impacts. To date, the focus of most commercial entities has been on development of hardware such as smart meters and green technologies. However, with the exception of a handful of academic researchers, there has been limited research on optimization, control and more broadly, the information science required to effectively utilize this hardware. Our proposal targets development of the basic information science that will effectively bridge this gap. Using our expertise in information theory, network analysis, infrastructure analysis and power engineering, we will develop new algorithms for optimal design of the nation's next generation power transmission and distribution system that address the realities and challenges of the future grid. We focus on three core information science challenges in Grid Design, Grid Control and Grid Stability by considering a set of challenging problems. First, we study efficient and robust integration of geographically widespread renewable generation by solving large, cutting-edge optimization problems that incorporate difficult constraints imposed by the transmission of electrical power. Second, we consider two load balancing problems in the low-voltage distribution grid: development of robust and efficient distributed control techniques and algorithms based on queuing theory. These tools will enable high penetration of small-scale distributed generation and plug-in hybrid vehicles with limited or no communications. We will also develop efficient control algorithms for maintaining an operational distribution grid via switching of redundant lines. Third, we will develop a preventive toolbox capable of detecting instabilities and failures prior to their occurrence. Damaging failures and cascading outages, which can result in billions of dollars worth of damage, have substantial economic consequences. We will focus on estimating the probability of these outages, identifying their signatures and precursors, and developing detection algorithms for alerting the transmission grid operator. The new challenges in IS&T for smart grids require coherent advances in analysis and control, stability and reliability metrics, state estimation, data aggregation and assimilation, and communication middleware for the grid as well as understanding consumer response.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2021

Control-Theory-Informed Feature Selection for Detecting Malicious Tampering in Additive Layer Manufacturing Processes

Additive layer manufacturing (ALM) is rapidly becoming an appealing solution to the low-volume manufacturing of metal, polymer, or composite parts. However, ALM’s reliance on digital part specifications, microcontrollers, and modern networking makes these devices vulnerable to malicious tampering by cyber attackers, which can negatively affect part performance and even result in catastrophic failure. We present a hybrid analytic approach to feature discovery using control theoretic techniques and linear modelling on input-output data collected from a representative controller system. Employing this approach, we design, train, and test an anomaly detection system. The preliminary results show that the proposed approach effectively discovers useful input-output relationships for anomaly detection in a simulated ALM process. Application to larger and more complex systems are discussed.

Dawson, Joel↗
DOE OSTI2017

Analyzing and Minimizing Capacity Fade through Optimal Model-Based Control - Theory and Experimental Validation

In order to significantly expand the BEV market, and to increase the use of lithium-ion batteries in electric grids, there is a need to develop optimal charging strategies to utilize the batteries more efficiently and enable longer life. Advanced battery management systems that can calculate and implement such strategies in real time are expected to play a critical role for this purpose. This article investigates different approaches for determining model-based optimal charging profiles for batteries, and experimentally validates the gain obtained using such profiles. Optimal profiles that maximize the cycle life of the cells are implemented on 16 Ah NMC cells for 30 minutes of charge followed by 5C discharge, and the cycle life is compared to a standard 2C CC-CV charge and 5C discharge. An improvement of more than 100% in cycle life is observed experimentally, for our test conditions on this cell design. This study is the first to experimentally demonstrate that the improved extra knowledge obtained by sophisticated physics-based models results in significant improvements in battery performance when employed in a real time control algorithm.

25 ENERGY STORAGE↗