Aerodynamic parameter estimation via Fourier modulating function techniques

Parameter estimation algorithms are developed in the frequency domain for systems modeled by input/output ordinary differential equations. The approach is based on Shinbrot's method of moment functionals utilizing Fourier based modulating functions. Assuming white measurement noises for linear multivariable system models, an adaptive weighted least squares algorithm is developed which approximates a maximum likelihood estimate and cannot be biased by unknown initial or boundary conditions in the data owing to a special property attending Shinbrot-type modulating functions. Application is made to perturbation equation modeling of the longitudinal and lateral dynamics of a high performance aircraft using flight-test data. Comparative studies are included which demonstrate potential advantages of the algorithm relative to some well established techniques for parameter identification. Deterministic least squares extensions of the approach are made to the frequency transfer function identification problem for linear systems and to the parameter identification problem for a class of nonlinear-time-varying differential system models

Parameter identification for nonlinear aerodynamic systems

Parameter identification for nonlinear aerodynamic systems is examined. It is presumed that the underlying model can be arranged into an input/output (I/O) differential operator equation of a generic form. The algorithm estimation is especially efficient since the equation error can be integrated exactly given any I/O pair to obtain an algebraic function of the parameters. The algorithm for parameter identification was extended to the order determination problem for linear differential system. The degeneracy in a least squares estimate caused by feedback was addressed. A method of frequency analysis for determining the transfer function G(j omega) from transient I/O data was formulated using complex valued Fourier based modulating functions in contrast with the trigonometric modulating functions for the parameter estimation problem. A simulation result of applying the algorithm is given under noise-free conditions for a system with a low pass transfer function

High-Accuracy and Fast-Response Flywheel Torque Control

Compared with current mode flywheel torque controller, speed mode torque controller has superior disturbance rejection capability. However, the speed loop delay reduces system dynamic response speed. To solve this problem, a two-degrees-of-freedom controller (2DOFC) which consists of a feedback controller (FBC) and a command feedforward controller (FFC) is proposed. The transfer function of FFC is found based on the inverse model of motor drive system, whose parameters are identified by recursive least squares (RLS) algorithm in real-time. Upon this, Kalman filter with softening factor is introduced for the improved parameters identification and torque control performances. Finally, the validity and the superiority of the proposed control scheme are verified through experiments with magnetically suspended flywheel (MSFW) motor

VIBRATION BASED DAMAGE IDENTIFICATION OF TIME-VARYING DYNAMICAL SYSTEMS

This thesis develops and explores two new kinds of vibration-based damage identification methodologies suitable for dynamical systems with periodically time-varying coefficients; 1) a Floquet based method (Methodology I) and, 2) a Sideband Frequency Response Function (FRF) method (Methodology II). One important class of dynamical systems where periodic time-varying parametric terms naturally arise is rotordynamic systems. For the case of a flexible shaft-rotor system with multiple open cracks, this thesis explores a new Least Squares damage identification approach based on Floquet theory with iterative eigenvector estimate updating. It is found that this method is able to detect the location and severity of multiple cracks with the assistance of control inputs from an Active Magnetic Bearing (AMB). However, it is also found that this method could not effectively identify the crack angle. To overcome this shortcoming, the new Sideband FRF based methodology is developed which utilizes the measured changes in transfer function magnitude and phase due to structural damage at the primary and side-band frequencies of the damaged periodically time-varying dynamical system. This method provides the advantages of arbitrary interrogation frequency and multiple inputs/outputs which greatly enriches the dataset for damage identification. This damage identification algorithm utilizes an iterative least square approach combined with a Newton-Raphson technique to estimate the damage parameters. The effectiveness of this method is thoroughly explored for a flexible rotor system and a planar truss both with breathing cracks. In each case, damage estimation is performed using time-domain vibration data taken from full nonlinear simulations of the cracked structures. The results show that this new method successfully estimated the crack depths, locations and angles for the case of multiple simultaneous damages

Indirect approach to continuous time system identification of food extruder

A three-stage approach to system identification in the continuous time is presented which is appropriate for day-to-day application by plant engineers in the process industry. The three stages are: data acquisition using relay feedback; non-parametric identification of the system step response; and parametric model fitting of the identified step response. The method is evaluated on a pilot-scale food-cooking extruder

Model estimation of cerebral hemodynamics between blood flow and volume changes: a data-based modeling approach

It is well known that there is a dynamic relationship between cerebral blood flow (CBF) and cerebral blood volume (CBV). With increasing applications of functional MRI, where the blood oxygen-level-dependent signals are recorded, the understanding and accurate modeling of the hemodynamic relationship between CBF and CBV becomes increasingly important. This study presents an empirical and data-based modeling framework for model identification from CBF and CBV experimental data. It is shown that the relationship between the changes in CBF and CBV can be described using a parsimonious autoregressive with exogenous input model structure. It is observed that neither the ordinary least-squares (LS) method nor the classical total least-squares (TLS) method can produce accurate estimates from the original noisy CBF and CBV data. A regularized total least-squares (RTLS) method is thus introduced and extended to solve such an error-in-the-variables problem. Quantitative results show that the RTLS method works very well on the noisy CBF and CBV data. Finally, a combination of RTLS with a filtering method can lead to a parsimonious but very effective model that can characterize the relationship between the changes in CBF and CBV