Nonlinear sensitivity coefficients and corrections in system identification
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A new method for frequency-domain identification of rotorcraft dynamics is presented. Nonparametric frequency-response identification and parametric tranfer-function modeling methods are extended to allow the extraction of state-space (stability and control derivative) representations. An interactive computer program DERIVID is described for the iterative solution of the multi-input/multi-output frequency-response matching approach used in the identification. Theoretical accuracy methods are used to determine the appropriate model structure and degree-of-confidence in the identified parameters. The method is applied to XV-15 tilt-rotor aircraft data in hover. Bare-airframe stability and control derivatives for the lateral/directional dynamics are shown to compare favorably with models previously obtained using time-domain identification methods and the XV-15 simulation program.
A modal test using active members as the excitation source has been performed on the Precision Truss. Using the step sine testing technique, the frequency response functions are obtained and the modal parameters are extracted by the curve-fitting method. Total of 10 global modes and 3 local modes are obtained. The results are compared with those obtained by the conventional external excitation test.
Data partitioning and modified stepwise regression were applied to recorded flight data from a Royal Aerospace Establishment high incidence research model. An aerodynamic model structure and corresponding stability and control derivatives were determined for angles of attack between 18 and 30 deg. Several nonlinearities in angles of attack and sideslip as well as a unique roll-dominated set of lateral modes were found. All flight estimated values were compared to available wind tunnel measurements.
A new method for frequency-domain identification of rotorcraft dynamics is presented. Nonparametric frequency-response identification and parametric transfer-function modeling methods are extended to allow the extraction of state-space (stability and control derivative) representations. An interactive computer program DERIVID is described for the iterative solution of the multi-input/multi-output frequency-response matching approach used in the identification. Theoretical accuracy methods are used to determine the appropriate model structure and degree-of-confidence in the identified parameters. The method is applied to XV-15 tilt-rotor aircraft data in hover. Bare-airframe stability and control derivatives for the lateral/directional dynamics are shown to compare favorably with models previously obtained using time-domain identification methods and the XV-15 simulation program.
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In the last few years various methods of identifying structural dynamics models from modal testing data have appeared. A comparison is presented of four of these algorithms: the Eigensystem Realization Algorithm (ERA), the modified version ERA/DC where DC indicated that it makes use of data correlation, the Q-Markov Cover algorithm, and an algorithm due to Moonen, DeMoor, Vandenberghe, and Vandewalle. The comparison is made using a five mode computer module of the 20 meter Mini-Mast truss structure at NASA Langley Research Center, and various noise levels are superimposed to produced simulated data. The results show that for the example considered ERA/DC generally gives the best results; that ERA/DC is always at least as good as ERA which is shown to be a special case of ERA/DC; that Q-Markov requires the use of significantly more data than ERA/DC to produce comparable results; and that is some situations Q-Markov cannot produce comparable results.
This paper describes recent advances in structural quieting technology as applied to active truss structures intended for high precision space based optics applications. Collocated active damping control loops are designed in order to impedance match piezoelectric active members to the structure. Noncollocated control loops are also studied in relation to controlling lightly damped structures.
In the last few years various methods of identifying structural dynamics models from modal testing data have appeared. This paper presents a comparison of four of these algorithms: the Eigensystem Realization Algorithm (ERA), the modified version ERA/DC where DC indicates that it makes use of data correlations, the Q-Markov Cover algorithm, and an algorithm due to Moonen, DeMoor, Vandenberghe and Vandewalle. The comparison is made using a five mode computer model of the 20 meter Mini-Mast truss structure at NASA Langley Research Center, and various noise levels are superimposed to produce simulated data. The results show that for the example considered ERA/DC generally gives the best results; that ERA/DC is always at least as good as ERA which is shown to be a special case of ERA/DC; that Q-Markov requires the use of significantly more data than ERA/DC to produce comparable results; and that in some situations Q-Markov cannot produce comparable results.
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The paper describes three approaches and possible variations for the determination of the Markov parameters for forced response data using general inputs. It is shown that, when the parameters in the solution procedure are bootstrapped, the results can be obtained very efficiently, but the errors propagate throughout all parameters. By arranging the data in a different form and using singular value decomposition, the resulting identified parameters are more accurate, in the least number of successive experiments, at the expense of a large matrix singular value decomposition. When a recursive procedure is employed, the calculations can be performed very efficiently, but the number of repetitions of the experiments is much greater for a given accuracy than for any of the previous approaches. An alternative formulation is proposed to combine the advantages of each of the approaches.
Artificial neural networks offer some interesting possibilities for use in control. Our current research is on the use of neural networks on an aircraft model. The model can then be used in a nonlinear control scheme. The effectiveness of network training is demonstrated.
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The primary objective of NASA's CSI program at JPL is to develop and demonstrate the CSI technology required to achieve high precision structural stability on large complex optical class spacecraft. The focus mission for this work is an orbiting interferometer telescope. Toward the realization of such a mission, a series of evolutionary testbed structures are being constructed. The JPL's CSI Phase B testbed is the second structure constructed in this series which is designed to study the pathlength control problem of the optical train of a stellar interferometer telescope mounted on a large flexible structure. A detailed description of this testbed can be found. This paper describes our efforts in the first phase of active structural control experiments of Phase B testbed using the active control approach where a single piezoelectric active member is used as an actuation device and the measurements include both colocated and noncolocated sensors. Our goal for this experiment is to demonstrate the feasibility of active structural control using both colocated and noncolocated measurements by means of successive control design and loop closing. More specifically, the colocated control loop was designed and closed first to provide good damping improvement over the frequency range of interest. The noncolocated controller was then designed with respect to a partially controlled structure to further improve the performance. Based on our approach, experimental closed-loop results have demonstrated significant performance improvement with excellent stability margins.
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This paper presents the use of matrix fraction descriptions for least-squares curve fitting of the frequency spectra to compute two matrix polynomials. The matrix polynomials are intermediate step to obtain a linearized representation of the experimental transfer function. Two approaches are presented: first, the matrix polynomials are identified using an estimated transfer function; second, the matrix polynomials are identified directly from the cross/auto spectra of the input and output signals. A set of Markov parameters are computed from the polynomials and subsequently realization theory is used to recover a minimum order state space model. Unevenly spaced frequency response functions may be used. Results from a simple numerical example and an experiment are discussed to highlight some of the important aspect of the algorithm.
Parameter identification and modeling are key elements of a design and operational flight strategy for control of flexible space structures. The emphasis of the identification program is on on-orbit applications to spacecraft control. High performance robust controllers will result from advanced design synthesis techniques and on-orbit identification/system tuning. Near term goals for the program include development of an integrated on-line processing capability for flexible body parameter identification, and validation with physical structure experiments.
This presentation will address an approach for using modal residues and roots, typically derived from vibration testing, to solve for the lumped parameters of a finite element model. The uniqueness of the approach is that the mass, stiffness, or damping matrices of a structure do not need to be known or estimated a-priori. Instead just the connectivity of the structure needs to be estimated. In addition, the presentation will demonstrate how models with orders larger than the number of modes obtained by the test program can be solved. The presentation will start by covering the basis of the approach, then illustrate a trivial application of how it works, and finally illustrate its use on a cantilevered beam using only a fraction of the mode to solve the mass, stiffness, and damping matrices.