Engineering topics

Giesy, D. P.

Publications and source records attributed to Giesy, D. P..

12 records

NASA NTRS2018

Reliability Analysis in the Presence of Aleatory Uncertainty

This paper proposes a method for modeling a system’s response using data. In contrast to approaches that identify a limit state function, we focus on the case in which not all uncertain parameters affecting the response are observable and the measured response is corrupted by noise. To this end, the system response is not characterized by a limit state function but instead by a Random Predictor Model (RPM) having a nonparametric structure. Consequently, the resulting failure probability is not a scalar but a random variable. This variable accounts for the aleatory contributions of the model-form uncertainty and the measurement noise into the response. Furthermore, we propose a framework that enables trading off the predicted range of failure probabilities resulting from such an analysis with a measure of risk. In this context, risk is the percentage of all predicted outcomes the analyst is willing to ignore. The reliability analysis of an aeroelastic structure subject to flutter is used to illustrate the ideas proposed.

Crespo, L. G.↗

NASA NTRS2000

Parameterization of Model Validating Sets for Uncertainty Bound Optimizations

Given measurement data, a nominal model and a linear fractional transformation uncertainty structure with an allowance on unknown but bounded exogenous disturbances, easily computable tests for the existence of a model validating uncertainty set are given. Under mild conditions, these tests are necessary and sufficient for the case of complex, nonrepeated, block-diagonal structure. For the more general case which includes repeated and/or real scalar uncertainties, the tests are only necessary but become sufficient if a collinearity condition is also satisfied. With the satisfaction of these tests, it is shown that a parameterization of all model validating sets of plant models is possible. The new parameterization is used as a basis for a systematic way to construct or perform uncertainty tradeoff with model validating uncertainty sets which have specific linear fractional transformation structure for use in robust control design and analysis. An illustrative example which includes a comparison of candidate model validating sets is given.

Lim, K. B.↗

NASA NTRS1998

Parameterization of Model Validating Sets for Uncertainty Bound Optimizations

Given experimental data and a priori assumptions on nominal model and a linear fractional transformation uncertainty structure, feasible conditions for model validation is given. All unknown but bounded exogenous inputs are assumed to occur at the plant outputs. With the satisfaction of the feasible conditions for model validation, it is shown that a parameterization of all model validating sets of plant models is possible. The new parameterization can be used as a basis for the development of a systematic way to construct model validating uncertainty models which have specific linear fractional transformation structure for use in robust control design and analysis. The proposed feasible condition (existence) test and the parameterization is computationally attractive as compared to similar tests currently available.

Lim, K. B.↗

NASA NTRS1997

On the Directional Dependence and Null Space Freedom in Uncertainty Bound Identification

In previous work, the determination of uncertainty models via minimum norm model validation is based on a single set of input and output measurement data. Since uncertainty bounds at each frequency is directionally dependent for multivariable systems, this will lead to optimistic uncertainty levels. In addition, the design freedom in the uncertainty model has not been utilized to further reduce uncertainty levels. The above issues are addressed by formulating a min- max problem. An analytical solution to the min-max problem is given to within a generalized eigenvalue problem, thus avoiding a direct numerical approach. This result will lead to less conservative and more realistic uncertainty models for use in robust control.

Lim, K. B.↗

NASA NTRS1997

Computation of LFT Uncertainty Bounds with Repeated Parametric Uncertainties

A new methodology in which linear fractional transformation uncertainty bounds are directly constructed for use in robust control design and analysis is proposed. Existence conditions for model validating solutions with or without repeated scalar uncertainty are given. The approach is based on minimax formulation to deal with multiple non-repeated structured uncertainty components subject to fixed levels of repeated scalar uncertainties. Input directional dependence and variations with different experiments are addressed by maximizing uncertainty levels over multiple experimental data sets. Preliminary results show that reasonable uncertainty bounds on structured non-repeated uncertainties can be identified directly from measurement data by assuming reasonable levels of repeated scalar uncertainties.

Lim, K. B.↗

NASA NTRS1986

Control/structures interaction study of two 300 KW dual-keel space station concepts

The results of an investigation of the influence of structural stiffness of the space station framework on the controllability of two 300 kw class, solar dynamic powered, dual-keel space station designs are presented. The two design concepts differed only in the truss bay dimensions of the structural framework of the stations. Two control studies were made: (1) A study of the interaction of the framework structural response with the reaction control system used for attitude control during an orbital reboost maneuver; and (2) A study of the stability of the space station attitude control system with sensors influenced by the elastic deformations of the station framework. Although both configurations had acceptable control characteristics, the configuration with the larger truss bay dimension and its increased structural stiffness had more attractive characteristics for pointing control of the solar dynamic system during reboost and for attitude control during normal in-orbit operations.

Young, J. W.↗

NASA NTRS1984

Tradeoff methods in multiobjective insensitive design of airplane control systems

The latest results of an ongoing study of computer-aided design of airplane control systems are given. Constrained minimization algorithms are used, with the design objectives in the constraint vector. The concept of Pareto optimiality is briefly reviewed. It is shown how an experienced designer can use it to find designs which are well-balanced in all objectives. Then the problem of finding designs which are insensitive to uncertainty in system parameters are discussed, introducing a probabilistic vector definition of sensitivity which is consistent with the deterministic Pareto optimal problem. Insensitivity is important in any practical design, but it is particularly important in the design of feedback control systems, since it is considered to be the most important distinctive property of feedback control. Methods of tradeoff between deterministic and stochastic-insensitive (SI) design are described, and tradeoff design results are presented for the example of the a Shuttle lateral stability augmentation system. This example is used because careful studies have been made of the uncertainty in Shuttle aerodynamics. Finally, since accurate statistics of uncertain parameters are usually not available, the effects of crude statistical models on SI designs are examined.

Schy, A. A.↗

NASA NTRS1983

Tradeoff studies in multiobjective insensitive design of airplane control systems

A computer aided design method for multiobjective parameter-insensitive design of airplane control systems is described. Methods are presented for trading off nominal values of design objectives against sensitivities of the design objectives to parameter uncertainties, together with guidelines for designer utilization of the methods. The methods are illustrated by application to the design of a lateral stability augmentation system for two supersonic flight conditions of the Shuttle Orbiter. Objective functions are conventional handling quality measures and peak magnitudes of control deflections and rates. The uncertain parameters are assumed Gaussian, and numerical approximations of the stochastic behavior of the objectives are described. Results of applying the tradeoff methods to this example show that stochastic-insensitive designs are distinctly different from deterministic multiobjective designs. The main penalty for achieving significant decrease in sensitivity is decreased speed of response for the nominal system.

Schy, A. A.↗

NASA NTRS1981

Multiobjective insensitive design of airplane control systems with uncertain parameters

A multiobjective computer-aided design algorithm has been developed which minimizes the sensitivity of the design objectives to uncertainties in system parameters. The more important uncertain parameters are described by a gaussian random vector with known covariance matrix, and a vector sensitivity objective function is defined as the probabilities that the design objectives will violate specified requirements constraints. Control system parameters are found which minimize the sensitivity vector in a Pareto-optimal sense, using constrained minimization algorithms. Example results are shown for lateral stability augmentation system (SAS) design for three Shuttle flight conditions.

Schy, A. A.↗

NASA NTRS1980

Pareto-optimal multi-objective design of airplane control systems

A constrained minimization algorithm for the computer aided design of airplane control systems to meet many requirements over a set of flight conditions is generalized using the concept of Pareto-optimization. The new algorithm yields solutions on the boundary of the achievable domain in objective space in a single run, whereas the older method required a sequence of runs to approximate such a limiting solution. However, Pareto-optimality does not guarantee a satisfactory design, since such solutions may emphasize some objectives at the expense of others. The designer must still interact with the program to obtain a well-balanced set of objectives. Using the example of a fighter lateral stability augmentation system (SAS) design over five flight conditions, several effective techniques are developed for obtaining well-balanced Pareto-optimal solutions. For comparison, one of these techniques is also used in a recently developed algorithm of Kreisselmeier and Steinhauser, which replaces the hard constraints with soft constraints, using a special penalty function. It is shown that comparable results can be obtained.

Schy, A. A.↗

NASA NTRS1979

A multiple objective optimization approach to aircraft control systems design

The design of an aircraft lateral control system, subject to several performance criteria and constraints, is considered. While in the previous studies of the same model a single criterion optimization, with other performance requirements expressed as constraints, has been pursued, the current approach involves a multiple criteria optimization. In particular, a Pareto optimal solution is sought.

Tabak, D.↗

NASA NTRS1978

Calculation of Pareto-optimal solutions to multiple-objective problems using threshold-of-acceptability constraints

A technique is presented for the calculation of Pareto-optimal solutions to a multiple-objective constrained optimization problem by solving a series of single-objective problems. Threshold-of-acceptability constraints are placed on the objective functions at each stage to both limit the area of search and to mathematically guarantee convergence to a Pareto optimum.

Giesy, D. P.↗