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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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At least 1,243 records · Page 69
All sourcesNASA NTRSDOE OSTI
NASA NTRS1976

Estimation of characteristics and stochastic control of an aircraft flying in atmospheric turbulence

An adaptive control technique to improve the flying qualities of an aircraft in turbulence was investigated. The approach taken was to obtain maximum likelihood estimates of the unknown coefficients of the aircraft system and then, using these estimates along with the separation principle, to define the stochastic optimal control. The maximum likelihood estimation technique that accounted for the effects of turbulence provided good estimates of the unknown coefficients and of the turbulence. The assessment of the stochastic optimal control based on the maximum likelihood estimates showed that the desired effects were attained for the regulator problem of minimizing pitch angle and the tracking problem of requiring normal acceleration to follow the pilot input.

Iliff, K. W.↗
NASA NTRS1992

A parameter optimization approach to controller partitioning for integrated flight/propulsion control application

A parameter optimization framework is presented to solve the problem of partitioning a centralized controller into a decentralized hierarchical structure suitable for integrated flight/propulsion control implementation. The controller partitioning problem is briefly discussed and a cost function to be minimized is formulated, such that the resulting 'optimal' partitioned subsystem controllers will closely match the performance (including robustness) properties of the closed-loop system with the centralized controller while maintaining the desired controller partitioning structure. The cost function is written in terms of parameters in a state-space representation of the partitioned sub-controllers. Analytical expressions are obtained for the gradient of this cost function with respect to parameters, and an optimization algorithm is developed using modern computer-aided control design and analysis software. The capabilities of the algorithm are demonstrated by application to partitioned integrated flight/propulsion control design for a modern fighter aircraft in the short approach to landing task. The partitioning optimization is shown to lead to reduced-order subcontrollers that match the closed-loop command tracking and decoupling performance achieved by a high-order centralized controller.

Schmidt, Phillip↗
NASA NTRS1993

A parameter optimization approach to controller partitioning for integrated flight/propulsion control application

A parameter optimization framework is presented to solve the problem of partitioning a centralized controller into a decentralized hierarchical structure suitable for integrated flight/propulsion control implementation. The controller partitioning problem is briefly discussed and a cost function to be minimized is formulated, such that the resulting 'optimal' partitioned subsystem controllers will closely match the performance (including robustness) properties of the closed-loop system with the centralized controller while maintaining the desired controller partitioning structure. The cost function is written in terms of parameters in a state-space representation of the partitioned sub-controllers. Analytical expressions are obtained for the gradient of this cost function with respect to parameters, and an optimization algorithm is developed using modern computer-aided control design and analysis software. The capabilities of the algorithm are demonstrated by application to partitioned integrated flight/propulsion control design for a modern fighter aircraft in the short approach to landing task. The partitioning optimization is shown to lead to reduced-order subcontrollers that match the closed-loop command tracking and decoupling performance achieved by a high-order centralized controller.

Schmidt, Phillip H.↗
DOE OSTI2023

Dancing the Quantum Waltz: Compiling Three-Qubit Gates on Four Level Architectures

Superconducting quantum devices are a leading technology for quantum computation, but they face several challenges. Gate errors, coherence errors and a lack of connectivity all contribute to low fidelity results. In particular, connectivity restrictions enforce a gate set that requires three-qubit gates to be decomposed into one- or two-qubit gates. This substantially increases the number of two-qubit gates that need to be executed. However, many quantum devices have access to higher energy levels. We can expand the qubit abstraction of |0〉 and |1〉 to a ququart which has access to the |2〉 and |3〉 state, but with shorter coherence times. This allows for two qubits to be encoded in one ququart, enabling increased virtual connectivity between physical units from two adjacent qubits to four fully connected qubits. This connectivity scheme allows us to more efficiently execute three-qubit gates natively between two physical devices.We present direct-to-pulse implementations of several three-qubit gates, synthesized via optimal control, for compilation of three-qubit gates onto a superconducting-based architecture with access to four-level devices with the first experimental demonstration of four-level ququart gates designed through optimal control. We demonstrate strategies that temporarily use higher level states to perform Toffoli gates and always use higher level states to improve fidelities for quantum circuits. We find that these methods improve expected fidelities with increases of 2x across circuit sizes using intermediate encoding, and increases of 3x for fully-encoded ququart compilation.

Litteken, Andrew↗
NASA NTRS1991

A new proof of the Jacobi necessary condition

A new proof is given for Jacobi's no-conjugate-point necessary condition. For a certain class of linear-quadratic optimal control problems it is shown that the existence of a conjugate point in the interior of the extremal implies the existence of control perturbations that lead to a reduction in cost. In a well-known way, through the concept of the accessory minimum problem, this results in a no-conjugate-point condition for general optimal control problems. Important ideas used in this work are adopted from Breakwell and Ho (1965). In contrast to earlier results, the new proof also applies if the coefficient functions of time associated with the accessory minimum problem have any finite number of discontinuities.

Seywald, Hans↗
DOE OSTI2017

The Salem Smart Power Center: An Assessment of Battery Performance and Economic Potential

This paper presents an assessment of the economic potential of a 5 MW/1.25 MWh Energy Storage System (ESS) installed at the Salem Smart Power Center (SSPC), a smart grid technology demonstration facility owned and operated by Portland General Electric (PGE) in Salem, Oregon. The ESS and the grid conditions in which it operates were modeled using Pacific Northwest National Laboratory’s Battery Storage Evaluation Tool (BSET) to explore tradeoffs between services, and to develop optimal control strategies. This assessment monetized the value derived from nine services the SSPC could provide to PGE and the customers it serves. The ESS and the grid conditions in which it operates were modeled using PNNL’s in-house optimization tool BSET to explore tradeoffs between services, and to develop optimal control strategies. The analysis resulted in a number of lessons that provide crucial insights into the practical application of ESS, including; The SSPC, which was originally conceived as a research and test facility and built with the prevailing maturity technology level, was built at a cost ($20.4 million) that exceeds current day prices ($5.4 million) for a similarly designed and built 5 MW/1.25 MWh system; In terms of economic operation, the SSPC is currently underutilized, deployed only for primary frequency response. PNNL modeling indicates that optimal operation of the ESS could generate an additional value of $2.3 million over 20 years. It should also be noted that primary frequency response is the highest benefit application but requires a response from the SSPC only 17 hours each year. While optimally engaged, the ESS would provide arbitrage and ancillary services 78 percent of the time, but those services generate only 27 percent of the total value; Participation in Western EIM represents an interesting opportunity for PGE with a potential to generate $2.1 million value in PV terms over 20 years in the 5-min real-time market; With an energy to power ratio of only 0.25, the SSPC is not well suited to engage in most energy-intensive applications such as arbitrage or ancillary services. By upsizing the storage capacity to 5 MWh and 10MWh, the additional value allows the benefits ($13.3 million and 20.3 million, respectively) to exceed the system’s revenue requirements ($11.5 and $16.4 million, respectively). For the SSPC, ROI ratios exceeded 1.0 when the energy to power ratio fell between 0.5 and 3.5, and peaked at an energy to power ratio of 2.0. This report represents the output of the first of a two-phase effort. Phase II will involve the development of enhanced control strategies to assist PGE in realizing the benefits of energy storage in real-time.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2021

TEAM Project Review, Year 2

This report summarizes our research activities within the TEAM project between December 2020 and December 2021, funded by the ASCR Advanced Research in Quantum Computing program. During the reporting period the LLNL-MSU team has made progress on several fronts. An overarching goal of the team is to provide a comprehensive suite of software tools that can be used for the Characterize-Optimize-Compute loop needed to implement and execute algorithms on quantum devices. We are concurrently developing lightweight solvers that can be used on desktop computers to find optimal control pulses and to characterize small quantum systems (consisting of a few transmons and cavities). However, desktop computers are insufficient for simulating and characterizing larger quantum systems. We have therefore also developed parallel, distributed memory, simulators and optimization solvers, both for open and closed quantum systems. These parallel solvers have, for example, been used to study quantum optimal control for pure-state preparation, utilizing 1000’s of cores on a modern high-performance computing (HPC) platform.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
NASA NTRSDate not supplied

Maximum-Likelihood Parameter Estimation for High-Contrast Wavefront Sensing & Control

Stellar coronagraphs use closed-loop focal-plane wavefront sensing and control algorithms to create high-contrast dark zones suitable for imaging exoplanets and exozodiacal dust clouds around nearby stars. At present, the deepest contrast has been achieved using model-based algorithms, which use the predicted focal-plane influence of the coronagraph's deformable mirrors to drive diffracted starlight toward zero over time in an optimal control framework. However, model-based algorithms are susceptible to model mismatch, wherein a departure of the coronagraph's true optical characteristics from the model predictions causes reduced control loop performance. Here, we report on a technique for maximum-likelihood estimation of the wavefront control Jacobian matrix and noise statistics of the coronagraph focal-plane electric field from data acquired in situ during closed-loop wavefront control operations. By empirically tuning the Jacobian and noise properties in a statistically rigorous fashion, the maximum-likelihood approach mitigates model mismatch and recovers near-optimal control loop performance.

coronagraphy↗
DOE OSTI2024

Leveraging Multitime Hamilton–Jacobi PDEs for Certain Scientific Machine Learning Problems

Hamilton-Jacobi partial differential equations (HJ PDEs) have deep connections with a wide range of fields, including optimal control, differential games, and imaging sciences. By considering the time variable to be a higher dimensional quantity, HJ PDEs can be extended to the multi-time case. In this paper, we establish a novel theoretical connection between specific optimization problems arising in machine learning and the multi-time Hopf formula, which corresponds to a representation of the solution to certain multi-time HJ PDEs. Through this connection, we increase the interpretability of the training process of certain machine learning applications by showing that when we solve these learning problems, we also solve a multi-time HJ PDE and, by extension, its corresponding optimal control problem. As a first exploration of this connection, we develop the relation between the regularized linear regression problem and the Linear Quadratic Regulator (LQR). We then leverage our theoretical connection to adapt standard LQR solvers (namely, those based on the Riccati ordinary differential equations) to design new training approaches for machine learning. Lastly, we provide some numerical examples that demonstrate the versatility and possible computational advantages of our Riccati-based approach in the context of continual learning, post-training calibration, transfer learning, and sparse dynamics identification.

97 MATHEMATICS AND COMPUTING↗
NASA NTRS1972

An Optimization study on a hybrid computer

The maximum principle is applied to minimum-time optimal-control problems, and an optimization algorithm is presented which can be implemented on a hybrid computer. The state and adjoint equations are set up on ASTRAC 2, a high-speed analog computer capable of 1000 differential equation solutions per second. The optimization algorithm is implemented on a PDP-9, an 18-bit, digital computer. The optimization scheme has global and local search phases and uses a vector optimization criterion. Second and third-order bang-bang control systems are studied as examples.

Gonzalez, R. S.↗
NASA NTRS1980

Implicit model following and parameter identification of unstable aircraft

A transformation in the s-plane is described which has utility in implicit model-following optimal control design application and in estimation or parameter identification problems. The objective of the transformation is, for the control problem, to achieve an unstable closed-loop system, and, for the estimation problem, to alleviate algorithm convergence problems that may arise in identifying unstable systems. For the control problem, the transformation is a shift along the real (sigma) axis of the plant and model poles and zeros. This transformation is shown to be equivalent to a modified performance index but offers the advantage of compatibility with existing optimal control solution algorithms. For the estimation problem, the data are multiplied by an exponential function and the assumed measurement and process noise covariances are appropriately modified. Examples of both control and estimation applications are presented.

Lebacqz, J. V.↗
NASA NTRS1981

Optimal digital control of multirate systems

Many digitally controlled aerospace systems have widely separated time constants and thus can benefit from the use of two or more sample rates. In this paper, the analysis and synthesis of multirate systems is accomplished by creating an equivalent single rate system and applying existing techniques. The optimal steady state solution of the single rate system is obtained by eigenvector decomposition and then used to compute the periodic solution to the Riccati equation of the original multirate system. An example shows when multirate analysis is necessary and the penalty of various levels of approximations to the exact multirate solution.

Amit, N.↗
NASA NTRS1990

Stochastic ordering properties and optimal routing control for a class of finite capacity queueing systems

The problem of routing jobs to parallel queues with identical exponential servers and unequal finite buffer capacities is considered. Stochastic ordering and weak majorization properties on critical performance measures are established by means of event-driven inductions. In particular, it is shown that the intuitive 'join the shortest non-full queue' (SNQ) policy is optimal with respect to an overall function that accounts for holding and blocking costs. Moreover, the buffer allocation problem is solved by proving the intuitive result that, for a fixed total buffer capacity, the optimal allocation scheme is the one in which the difference between the maximum and minimum queue capacities is minimized, i.e., becomes either 0 or 1.

Towsley, Don↗
NASA NTRS1976

Characteristics of the boundary-layer equations of the minimum time-to-climb problem

In many singular perturbation solutions of optimal control problems, the most difficult numerical task is to solve the boundary-layer equations. However, these equations have a special structure that may often be used to expedite their solution. This paper begins by noting the general nature of the boundary-layer equations for optimal control problems. These results are then applied to the aircraft minimum time-to-climb problem. A specific numerical example is considered to illustrate the characteristics of the solution of the boundary-layer equations for this problem.

Ardema, M. D.↗
NASA NTRS1972

Control system optimization studies. Volume 2: High frequency cutoff filter analysis

The problem of digital implementation of a cutoff filter is approached with consideration to word length, sampling rate, accuracy requirements, computing time and hardware restrictions. Computing time and hardware requirements for four possible programming forms for the linear portions of the filter are determined. Upper bounds for the steady state system output error due to quantization for digital control systems containing a digital network programmed both in the direct form and in the canonical form are derived. This is accomplished by defining a set of error equations in the z domain and then applying the final value theorem to the solution. Quantization error was found to depend upon the digital word length, sampling rate, and system time constants. The error bound developed may be used to estimate the digital word length and sampling rate required to achieve a given system specification. From the quantization error accumulation, computing time and hardware point of view, and the fact that complex poles and zeros must be realized, the canonical form of programming seems preferable.

Fong, M. H.↗
NASA NTRS1989

Computational complexities and storage requirements of some Riccati equation solvers

The linear optimal control problem of an nth-order time-invariant dynamic system with a quadratic performance functional is usually solved by the Hamilton-Jacobi approach. This leads to the solution of the differential matrix Riccati equation with a terminal condition. The bulk of the computation for the optimal control problem is related to the solution of this equation. There are various algorithms in the literature for solving the matrix Riccati equation. However, computational complexities and storage requirements as a function of numbers of state variables, control variables, and sensors are not available for all these algorithms. In this work, the computational complexities and storage requirements for some of these algorithms are given. These expressions show the immensity of the computational requirements of the algorithms in solving the Riccati equation for large-order systems such as the control of highly flexible space structures. The expressions are also needed to compute the speedup and efficiency of any implementation of these algorithms on concurrent machines.

Utku, Senol↗