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All sourcesNASA NTRSDOE OSTI
NASA NTRS1990

Reduced order component models for flexible multibody dynamics simulations

Many flexible multibody dynamics simulation codes require some form of component description that properly characterizes the dynamic behavior of the system. A model reduction procedure for producing low order component models for flexible multibody simulation is described. Referred to as projection and assembly, the method is a Rayleigh-Ritz approach that uses partitions of the system modal matrix as component Ritz transformation matrices. It is shown that the projection and assembly method yields a reduced system model that preserves a specified set of the full order system modes. Unlike classical component mode synthesis methods, the exactness of the method described is obtained at the expense of having to compute the full order system modes. The paper provides a comprehensive description of the method, a proof of exactness, and numerical results demonstrating the method's effectiveness.

Tsuha, Walter S.↗
NASA NTRS1991

Krylov model reduction algorithm for undamped structural dynamics systems

Krylov vectors furnish an efficient basis for eigenvalue analysis and model reduction of structural dynamics systems. The reduced-order model obtained by the present Krylov model-reduction algorithm for an undamped structural-dynamics system is found to match low-frequency moments. The transformed system equation in Krylov coordinates reflects the structure of a tandem system.

Craig, Roy R., Jr.↗
NASA NTRS1992

Control-structure interaction

The feasibility of using conventional proportional-integral-derivative (PID) control and an alternative optimal control to perform the pointing and tracking functions of the Space Station solar dynamic power module is investigated. A very large state model of 6 rigid body modes and 272 flexible modes is used in conjunction with classical linear-quadratic-Gaussian (LQG) optimal control to produce a full-order controller that satisfies the requirements. The results are compared with a classically designed PID controller that was implemented for a much smaller (6 rigid body, 40 flexible modes) model. The conventional control design approach is shown to be very much influenced by the order reduction of the plant model, i.e., the number of retained elastic modes from the full-order model, suggesting that for a complex, large space structure, such as the Space Station Freedom solar dynamic module, application of conventional control system design methods may not be adequate. The use of LQG control is recommended, and method for solving the large matrix. Riccati equation that arises from the optimal formulation is provided.

Cheng, Joseph K.↗
NASA NTRS1989

Component model reduction via the projection and assembly method

The problem of acquiring a simple but sufficiently accurate model of a dynamic system is made more difficult when the dynamic system of interest is a multibody system comprised of several components. A low order system model may be created by reducing the order of the component models and making use of various available multibody dynamics programs to assemble them into a system model. The difficulty is in choosing the reduced order component models to meet system level requirements. The projection and assembly method, proposed originally by Eke, solves this difficulty by forming the full order system model, performing model reduction at the the system level using system level requirements, and then projecting the desired modes onto the components for component level model reduction. The projection and assembly method is analyzed to show the conditions under which the desired modes are captured exactly; to the numerical precision of the algorithm.

Bernard, Douglas E.↗
NASA NTRS1993

Model reduction for the dynamics and control of large structural systems via neutral network processing direct numerical optimization

Three neural network processing approaches in a direct numerical optimization model reduction scheme are proposed and investigated. Large structural systems, such as large space structures, offer new challenges to both structural dynamicists and control engineers. One such challenge is that of dimensionality. Indeed these distributed parameter systems can be modeled either by infinite dimensional mathematical models (typically partial differential equations) or by high dimensional discrete models (typically finite element models) often exhibiting thousands of vibrational modes usually closely spaced and with little, if any, damping. Clearly, some form of model reduction is in order, especially for the control engineer who can actively control but a few of the modes using system identification based on a limited number of sensors. Inasmuch as the amount of 'control spillover' (in which the control inputs excite the neglected dynamics) and/or 'observation spillover' (where neglected dynamics affect system identification) is to a large extent determined by the choice of particular reduced model (RM), the way in which this model reduction is carried out is often critical.

Becus, Georges A.↗
NASA NTRS1995

Optimal guidance law development for an advanced launch system

The objective of this research effort was to develop a real-time guidance approach for launch vehicles ascent to orbit injection. Various analytical approaches combined with a variety of model order and model complexity reduction have been investigated. Singular perturbation methods were first attempted and found to be unsatisfactory. The second approach based on regular perturbation analysis was subsequently investigated. It also fails because the aerodynamic effects (ignored in the zero order solution) are too large to be treated as perturbations. Therefore, the study demonstrates that perturbation methods alone (both regular and singular perturbations) are inadequate for use in developing a guidance algorithm for the atmospheric flight phase of a launch vehicle. During a second phase of the research effort, a hybrid analytic/numerical approach was developed and evaluated. The approach combines the numerical methods of collocation and the analytical method of regular perturbations. The concept of choosing intelligent interpolating functions is also introduced. Regular perturbation analysis allows the use of a crude representation for the collocation solution, and intelligent interpolating functions further reduce the number of elements without sacrificing the approximation accuracy. As a result, the combined method forms a powerful tool for solving real-time optimal control problems. Details of the approach are illustrated in a fourth order nonlinear example. The hybrid approach is then applied to the launch vehicle problem. The collocation solution is derived from a bilinear tangent steering law, and results in a guidance solution for the entire flight regime that includes both atmospheric and exoatmospheric flight phases.

Calise, Anthony J.↗
NASA NTRS1993

Two-Stage Reduction Of Dynamical Models

No longer necessary to solve eigenvalue problems of high order. Component-mode projection-and-assembly model-reduction (COMPARE) method provides approximation of dynamics of vibrations of complicated, multiple flexible bodies by use of mathematical models of reduced order. Incorporates component-mode synthesis (CMS) method and enhanced projection-and-assembly (EP&A) method, described in "Enhanced Method of Reduction of Dynamical Models" (NPO-18402), providing for somewhat simplified two-stage process in which order of applicable mathematical models reduced. Reduced-order models used to design algorithms of control systems to suppress vibrations or otherwise control structure.

Lee, Allan Y.↗
NASA NTRS1985

On-orbit systems identification of flexible spacecraft

The process of on-orbit systems identification of flexible spacecraft is examined in terms of the difficulties that are expected because of the potentially unmanageable number of unknown model parameters due to the very high order system models involved. A Jordon block canonical form and global model parameters are used to reduce the number of unknown parameters to manageable numbers. A Bayesian approach is discussed which enables the merging of theoretical models with ground or on-orbit test results by using Unconditional Maximum Likelihood Parameter Estimation. A Modified Newton-Raphson technique is proposed for determining the model parameter estimates and an expected fit error criterion is recommended for the determination of the model structure and order reduction.

Taylor, L. W., Jr.↗
NASA NTRS2006

Computational Aeroelastic Modeling of Airframes and TurboMachinery: Progress and Challenges

Computational analyses such as computational fluid dynamics and computational structural dynamics have made major advances toward maturity as engineering tools. Computational aeroelasticity is the integration of these disciplines. As computational aeroelasticity matures it too finds an increasing role in the design and analysis of aerospace vehicles. This paper presents a survey of the current state of computational aeroelasticity with a discussion of recent research, success and continuing challenges in its progressive integration into multidisciplinary aerospace design. This paper approaches computational aeroelasticity from the perspective of the two main areas of application: airframe and turbomachinery design. An overview will be presented of the different prediction methods used for each field of application. Differing levels of nonlinear modeling will be discussed with insight into accuracy versus complexity and computational requirements. Subjects will include current advanced methods (linear and nonlinear), nonlinear flow models, use of order reduction techniques and future trends in incorporating structural nonlinearity. Examples in which computational aeroelasticity is currently being integrated into the design of airframes and turbomachinery will be presented.

Bartels, R. E.↗
NASA NTRS1990

Projection and assembly method for multibody component model reduction

The problem addressed is that of obtaining reduced-order component models for use in simulating the dynamics of a multibody system. In certain cases, nonlinear system models may be constructed using linear dynamic models for each component, but allowing large angle motion between components. Without some form of model reduction, system models constructed in this manner may be too large for use in control system design and simulation trades. This paper analyzes one method of component model reduction that allows systems level requirements (e.g., capturing the effect of body 1 reaction wheel noise on body 2 camera pointing) to aid in the selection of the reduced-order component models. Briefly stated, important modes are selected at the system level and projected onto the components, and reduced-order components are then assembled into a reduced-order system model that retains the projected modes.

Bernard, Douglas E.↗
NASA NTRS1989

Stochastic model of the NASA/MSFC ground facility for large space structures with uncertain parameters: The maximum entropy approach, part 2

A validated technology data base is being developed in the areas of control/structures interaction, deployment dynamics, and system performance for Large Space Structures (LSS). A Ground Facility (GF), in which the dynamics and control systems being considered for LSS applications can be verified, was designed and built. One of the important aspects of the GF is to verify the analytical model for the control system design. The procedure is to describe the control system mathematically as well as possible, then to perform tests on the control system, and finally to factor those results into the mathematical model. The reduction of the order of a higher order control plant was addressed. The computer program was improved for the maximum entropy principle adopted in Hyland's MEOP method. The program was tested against the testing problem. It resulted in a very close match. Two methods of model reduction were examined: Wilson's model reduction method and Hyland's optimal projection (OP) method. Design of a computer program for Hyland's OP method was attempted. Due to the difficulty encountered at the stage where a special matrix factorization technique is needed in order to obtain the required projection matrix, the program was successful up to the finding of the Linear Quadratic Gaussian solution but not beyond. Numerical results along with computer programs which employed ORACLS are presented.

Hsia, Wei Shen↗
NASA NTRS1991

Control-structure interaction study for the Space Station solar dynamic power module

The authors investigate the feasibility of using a conventional PID (proportional plus integral plus derivative) controller design to perform the pointing and tracking functions for the Space Station Freedom solar dynamic power module. Using this simple controller design, the control/structure interaction effects were also studied without assuming frequency bandwidth separation. From the results, the feasibility of a simple solar dynamic control solution with a reduced-order model, which satisfies the basic system pointing and stability requirements, is suggested. However, the conventional control design approach is shown to be very much influenced by the order of reduction of the plant model, i.e., the number of the retained elastic modes from the full-order model. This suggests that, for complex large space structures, such as the Space Station Freedom solar dynamic, the conventional control system design methods may not be adequate.

Cheng, J.↗
NASA NTRS1985

Model Reduction for Control System Design

An approach and a technique for effectively obtaining reduced order mathematical models of a given large order model for the purposes of synthesis, analysis and implementation of control systems is developed. This approach involves the use of an error criterion which is the H-infinity norm of a frequency weighted error between the full and reduced order models. The weightings are chosen to take into account the purpose for which the reduced order model is intended. A previously unknown error bound in the H-infinity norm for reduced order models obtained from internally balanced realizations was obtained. This motivated further development of the balancing technique to include the frequency dependent weightings. This resulted in the frequency weighted balanced realization and a new model reduction technique. Two approaches to designing reduced order controllers were developed. The first involves reducing the order of a high order controller with an appropriate weighting. The second involves linear quadratic Gaussian synthesis based on a reduced order model obtained with an appropriate weighting.

Enns, D. F.↗
NASA NTRS1989

Model reduction for flexible space structures

This paper presents the conditions under which modal truncation yields a near-optimal reduced-order model for a flexible structure. Next, a robust model reduction technique to cope with the damping uncertainties typical of flexible space structure is developed. Finally, a flexible truss and the COFS-1 structure are used to give realistic applications for the model reduction techniques studied in the paper.

Gawronski, Wodek↗
NASA NTRS1991

Numerical and literal aeroelastic-vehicle-model reduction for feedback control synthesis

The simplification of a high-order, literal model for large flexible aircraft is discussed. Areas of model fidelity that are critical if the model is to be used for control law synthesis are presented. Several simplification techniques, some new and some widely available, that can deliver the necessary model fidelity are presented and applied to a model from the literature. The techniques include both numerical and analytical approaches. An analytical approach, based on first-order sensitivity theory, is shown to lead not only to excellent numerical results, but also to closed-form analytical expressions for key system dynamic properties such as the pole/zero factors of the vehicle transfer-function matrix. The analytical results are expressed in terms of vehicle vibrational characteristics and rigid-body and aeroelastic stability derivatives, thus providing insight into the underlying causes for critical dynamic characteristics.

Newman, Brett↗
NASA NTRS1993

Component modes damping assignment methodology for articulated, multiflexible body structures

To simulate the dynamical motion of articulated, multiflexible body structures, one can use multibody simulation packages such as DISCOS. To this end, one must supply appropriate reduced-order models for all of the flexible components involved. The component modes projection and assembly model reduction (COMPARE) methodology is one way to construct these reduced-order component models, which when reassembled capture important system input-to-output mapping of the full-order model at multiple system configurations of interest. In conjunction, we must also supply component damping matrices which when reassembled generate a system damping matrix that has certain desirable properties. The problem of determining the damping factors of components' modes to achieve a given system damping matrix is addressed here. To this end, we must establish from first principles a matrix-algebraic relation between the system's modal damping matrix and the components' modal damping matrices. An unconstrained/constrained optimization problem can then be formulated to determine the component modes' damping factors that best satisfy that matrix-algebraic relation. The effectiveness of the developed methodology, called ModeDamp, has been successfully demonstrated on a high-order, finite element model of the Galileo spacecraft.

Lee, Allan Y.↗
NASA NTRS1993

Model reduction in integrated controls-structures design

It is the objective of this paper to present a model reduction technique developed for the integrated controls-structures design of flexible structures. Integrated controls-structures design problems are typically posed as nonlinear mathematical programming problems, where the design variables consist of both structural and control parameters. In the solution process, both structural and control design variables are constantly changing; therefore, the dynamic characteristics of the structure are also changing. This presents a problem in obtaining a reduced-order model for active control design and analysis which will be valid for all design points within the design space. In other words, the frequency and number of the significant modes of the structure (modes that should be included) may vary considerably throughout the design process. This is also true as the locations and/or masses of the sensors and actuators change. Moreover, since the number of design evaluations in the integrated design process could easily run into thousands, any feasible order-reduction method should not require model reduction analysis at every design iteration. In this paper a novel and efficient technique for model reduction in the integrated controls-structures design process, which addresses these issues, is presented.

Maghami, Peiman G.↗
NASA NTRS1992

A component modes projection and assembly model reduction methodology for articulated, multi-flexible body structures

A two-stage model reduction methodology, combining the classical Component Mode Synthesis (CMS) method and the newly developed Enhanced Projection and Assembly (EP&A) method is proposed in this research. The first stage of this methodology, called COmponent Mode Projection and Assembly model REduction (COMPARE) method, involves the generations of CMS mode sets, such as the MacNeal-Rubin mode sets. These mode sets are then used to reduce the order of each component model in the Rayleigh-Ritz sense. The resultant component models are then combined to generate reduced-order system models at various system configurations. A composite mode set which retains important system modes at all system configurations is then selected from these reduced-order system models. In the second stage, the EP&A model reduction method is employed to reduce further the order of the system model generated in the first stage. The effectiveness of the COMPARE methodology has been successfully demonstrated on a high-order, finite-element model of the cruise-configured Galileo spacecraft.

Lee, Allan Y.↗