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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 109 records · Page 6

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NASA NTRS1974

Earth resources data analysis program, phase 2

The efforts and findings of the Earth Resources Data Analysis Program are summarized. Results of a detailed study of the needs of EOD with respect to an applications development system (ADS) for the analysis of remotely sensed data, including an evaluation of four existing systems with respect to these needs are described. Recommendations as to possible courses for EOD to follow to obtain a viable ADS are presented. Algorithmic development comprised of several subtasks is discussed. These subtasks include the following: (1) two algorithms for multivariate density estimation; (2) a data smoothing algorithm; (3) a method for optimally estimating prior probabilities of unclassified data; and (4) further applications of the modified Cholesky decomposition in various calculations. Little effort was expended on task 3, however, two reports were reviewed.

Source record↗

NASA NTRS1985

Multivariable control of VTOL aircraft for shipboard landing

The problem of the automatic landing of VTOL aircraft on small ships is considered. Linear quadratic optimal control theory is used to design a VTOL ship motion tracking controller. Optimal root-loci and step responses are obtained to study the dynamics of the closed-loop system. Standard deviations of the ship motion tracking errors, and of the VTOL control amplitudes are computed, illustrating the tradeoff between accurate tracking, and limited control authority. Multivariable robustness margins are also obtained. The tracking of the vertical motion presents the difficulty of requiring large variations of the VTOL total thrust, a control which is limited both in amplitude and in bandwidth. Lateral controls are less restricted, but the motions are strongly coupled, with some adverse couplings in the ship motions, and in the aircraft dynamics. The advantage of the LQ control theory is demonstrated however, by its ability to account for these couplings in a robust manner, and, when possible, to use them to limit the control amplitudes.

Bodson, M.↗

NASA NTRS1997

Adaptive Performance Seeking Control Using Fuzzy Model Reference Learning Control and Positive Gradient Control

Performance Seeking Control attempts to find the operating condition that will generate optimal performance and control the plant at that operating condition. In this paper a nonlinear multivariable Adaptive Performance Seeking Control (APSC) methodology will be developed and it will be demonstrated on a nonlinear system. The APSC is comprised of the Positive Gradient Control (PGC) and the Fuzzy Model Reference Learning Control (FMRLC). The PGC computes the positive gradients of the desired performance function with respect to the control inputs in order to drive the plant set points to the operating point that will produce optimal performance. The PGC approach will be derived in this paper. The feedback control of the plant is performed by the FMRLC. For the FMRLC, the conventional fuzzy model reference learning control methodology is utilized, with guidelines generated here for the effective tuning of the FMRLC controller.

Kopasakis, George↗

NASA NTRS1985

Robust detection, isolation, and accommodation for sensor failures

Recent advances in multivariable robust control system design are extended to sensor failure, detection, isolation, and accommodation (FDIA) and estimator design. A new concept called threshold selector is introduced. It represents a significant and innovative tool for the analysis and synthesis of FDIA algorithms. Analytical results are obtained for the SISO case to compute optimal thresholds and size of minimum detectable failures, and a computer-aided technique is developed for the multivariable case. The techniques have been applied to sensor FDIA for an aircraft turbine engine control system.

Emami-Naeini, A.↗

NASA NTRS2011

Safe Onboard Guidance and Control Under Probabilistic Uncertainty

An algorithm was developed that determines the fuel-optimal spacecraft guidance trajectory that takes into account uncertainty, in order to guarantee that mission safety constraints are satisfied with the required probability. The algorithm uses convex optimization to solve for the optimal trajectory. Convex optimization is amenable to onboard solution due to its excellent convergence properties. The algorithm is novel because, unlike prior approaches, it does not require time-consuming evaluation of multivariate probability densities. Instead, it uses a new mathematical bounding approach to ensure that probability constraints are satisfied, and it is shown that the resulting optimization is convex. Empirical results show that the approach is many orders of magnitude less conservative than existing set conversion techniques, for a small penalty in computation time.

Blackmore, Lars James↗

NASA NTRS1968

Application of multivariable search techniques to the shaping of minimum total heat re-entry bodies at hyberbolic velocity

Heat minimization and optimal shapes for bodies of revolution entering earth atmosphere at hyperbolic speeds

Hague, D. S.↗

NASA NTRS1984

Applications of numerical optimization methods to helicopter design problems: A survey

A survey of applications of mathematical programming methods is used to improve the design of helicopters and their components. Applications of multivariable search techniques in the finite dimensional space are considered. Five categories of helicopter design problems are considered: (1) conceptual and preliminary design, (2) rotor-system design, (3) airframe structures design, (4) control system design, and (5) flight trajectory planning. Key technical progress in numerical optimization methods relevant to rotorcraft applications are summarized.

Miura, H.↗

NASA NTRS1985

Applications of numerical optimization methods to helicopter design problems - A survey

Miura, H.↗

NASA NTRS1984

Applications of numerical optimization methods to helicopter design problems - A survey

Miura, H.↗

NASA NTRS1975

Problems, questions and results in the use of the BBN-model

A hierarchical structured control model was used to describe pilot performance in a multivariable control problem. The model is based on the assumption that a well trained operator optimized a quadratic optimization criterion. The model considers some special characteristics of the human operator as time delay, neuromuscular time lag, observation and motor noise and the ability to extract the first derivative of a displaced value. Numerical results of a simple human engineering example using the BBN model demonstrate the influence of the variation of different parameters on covariance matrices of the state and observation vectors.

Dey, D.↗

NASA NTRS1976

Digital controllers for VTOL aircraft

Using linear-optimal estimation and control techniques, digital-adaptive control laws have been designed for a tandem-rotor helicopter which is equipped for fully automatic flight in terminal area operations. Two distinct discrete-time control laws are designed to interface with velocity-command and attitude-command guidance logic, and each incorporates proportional-integral compensation for non-zero-set-point regulation, as well as reduced-order Kalman filters for sensor blending and noise rejection. Adaptation to flight condition is achieved with a novel gain-scheduling method based on correlation and regression analysis. The linear-optimal design approach is found to be a valuable tool in the development of practical multivariable control laws for vehicles which evidence significant coupling and insufficient natural stability.

Stengel, R. F.↗

NASA NTRS2017

Anthropometry and Biomechanics Facility Presentation to Open EVA Research Forum

NASA is required to accommodate individuals who fall within a 1st to 99th percentile range on a variety of critical dimensions. The hardware the crew interacts with must therefore be designed and verified to allow these selected individuals to complete critical mission tasks safely and at an optimal performance level. Until now, designers have been provided simpler univariate critical dimensional analyses. The multivariate characteristics of intra-individual and inter-individual size variation must be accounted for, since an individual who is 1st percentile in one body dimension will not be 1st percentile in all other dimensions. A more simplistic approach, assuming every measurement of an individual will fall within the same percentile range, can lead to a model that does not represent realistic members of the population. In other words, there is no '1st percentile female' or '99th percentile male', and designing for these unrealistic body types can lead to hardware issues down the road. Furthermore, due to budget considerations, designers are normally limited to providing only 1 size of a prototype suit, thus requiring other possible means to ensure that a given suit architecture would yield the necessary suit sizes to accommodate the entire user population. Fortunately, modeling tools can be used to more accurately model the types of human body sizes and shapes that will be encountered in a population. Anthropometry toolkits have been designed with a variety of capabilities, including grouping the population into clusters based on critical dimensions, providing percentile information given test subject measurements, and listing measurement ranges for critical dimensions in the 1st-99th percentile range. These toolkits can be combined with full body laser scans to allow designers to build human models that better represent the astronaut population. More recently, some rescaling and reposing capabilities have been developed, to allow reshaping of these static laser scans in more representative postures, such as an abducted shoulder. All of the hardware designed for use with the crew must be sized to accommodate the user population, but the interaction between subject size and hardware fit is complicated with multi-component, complex systems like a space suit. Again, prototype suits are normally only provided in a limited size range, and suited testing is an expensive endeavor; both of these factors limit the number and size of people who can be used to benchmark a spacesuit. However, modeling tools for assessing suit-human interaction can allow potential issues to be modeled and visualized. These types of modeling tools can be used for analysis of a larger combination of anthropometries and hardware types than could feasibly be done with actual human subjects and physical mockups.

Rajulu, Sudhakar↗

NASA NTRS2021

Probabilistic Machine Learning Estimation of Ocean Mixed Layer Depth from Dense Satellite and Sparse In-Situ Observations

The ocean mixed layer plays an important role in the coupling between the upper ocean and atmosphere across a wide range of time scales. Estimation of the variability of the ocean mixed layer is therefore important for atmosphere-ocean prediction and analysis. The increasing coverage of in situ Argo profile data allows for an increasingly accurate analysis of the mixed layer depth (MLD) variability associated with deviations from the seasonal climatology. However, sampling rates are not sufficient to fully resolve subseasonal (<90 day) MLD variability. Yet, many multivariate observations-based analyses include implicit modeled subseasonal MLD variability. One analysis method is optimal interpolation of in situ data, but the interior analysis can be improved by leveraging surface data with regression or variational approaches. Here, we demonstrate how machine learning methods and satellite sea surface temperature, salinity, and height facilitate MLD estimation in a pilot study of two regions: the mid-latitude southern Indian and the eastern equatorial Pacific Oceans. We construct multiple machine learning architectures to produce weekly 1/2° gridded MLD anomaly fields (relative to a monthly climatology) with uncertainty estimates. We test multiple traditional and probabilistic machine learning techniques to compare both accuracy and probabilistic calibration. We validate our methodology by applying it to ocean model simulations. We find that incorporating sea surface data through a machine learning model improves the performance of spatiotemporal MLD variability estimation compared to optimal interpolation of Argo observations alone. These preliminary results are a promising first step for the application of machine learning to MLD prediction.

Machine Learning↗

NASA NTRS1988

A method for designing robust multivariable feedback systems

A new methodology is developed for the synthesis of linear, time-invariant (LTI) controllers for multivariable LTI systems. The aim is to achieve stability and performance robustness of the feedback system in the presence of multiple unstructured uncertainty blocks; i.e., to satisfy a frequency-domain inequality in terms of the structured singular value. The design technique is referred to as the Causality Recovery Methodology (CRM). Starting with an initial (nominally) stabilizing compensator, the CRM produces a closed-loop system whose performance-robustness is at least as good as, and hopefully superior to, that of the original design. The robustness improvement is obtained by solving an infinite-dimensional, convex optimization program. A finite-dimensional implementation of the CRM was developed, and it was applied to a multivariate design example.

Milich, David Albert↗

NASA NTRS1988

A method for designing robust multivariable feedback systems

A new methodology is developed for the synthesis of linear, time-invariant (LTI) controllers for multivariable LTI systems. The aim is to achieve stability and performance robustness of the feedback system in the presence of multiple unstructured uncertainty blocks; i.e., to satisfy a frequency-domain inequality in terms of the structured singular value. The design technique is referred to as the causality recovery methodology (CRM). Starting with an initial (nominally) stabilizing compensator, the CRM produces a closed-loop system whose performance-robustness is at least as good as, and hopefully superior to, that of the original design. The robustness improvement is obtained by solving an infinite-dimensional, convex optimization program. A finite-dimensional implementation of the CRM was developed, and it was applied to a multivariate design example.

Milich, David A.↗

NASA NTRS1987

Polynomial compensation, inversion, and approximation of discrete time linear systems

The least-squares transformation of a discrete-time multivariable linear system into a desired one by convolving the first with a polynomial system yields optimal polynomial solutions to the problems of system compensation, inversion, and approximation. The polynomial coefficients are obtained from the solution to a so-called normal linear matrix equation, whose coefficients are shown to be the weighting patterns of certain linear systems. These, in turn, can be used in the recursive solution of the normal equation.

Baram, Yoram↗

NASA NTRS1981

The Total Synthesis Problem of linear multivariable control. II - Unity feedback and the design morphism

Zames (1981) has observed that there is, in general, no 'separation principle' to guarantee optimality of a division between control law design and filtering of plant uncertainty. Peczkowski and Sain (1978) have solved a model matching problem using transfer functions. Taking into consideration this investigation, Peczkowski et al. (1979) proposed the Total Synthesis Problem (TSP), wherein both the command/output-response and command/control-response are to be synthesized, subject to the plant constraint. The TSP concept can be subdivided into a Nominal Design Problem (NDP), which is not dependent upon specific controller structures, and a Feedback Synthesis Problem (FSP), which is. Gejji (1980) found that NDP was characterized in terms of the plant structural matrices and a single, 'good' transfer function matrix. Sain et al. (1981) have extended this NDP work. The present investigation is concerned with a study of FSP for the unity feedback case. NDP, together with feedback synthesis, is understood as a Total Synthesis Problem.

Sain, M. K.↗

NASA NTRS1980

Alternatives for jet engine control

Alternatives to linear quadratic regulator theory in the linear case are examined along with nonlinear modelling and optimization approaches for global control. Context for the studies has been set by the DYNGEN digital simulator and by models generated for various phases of the F100 Multivariable Control Synthesis Program. With respect to the linear alternatives, the multivariable frequency domain is stressed. Progress is reported in both the direct algebraic approach to exact model matching, by means of stimulating work on the basic computational issues, and in the indirect generalized Nyquist approach. With respect to nonlinear modelling and optimization, the emphasis is twofold: the development of analytical nonlinear models of the jet engine and the use of these models in conjunction with techniques of mathematical programming in order to study global control over nonincremental portions of the flight envelope. The possibility of using tensor methods is explored.

Sain, M. K.↗