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Calibri 83ffff̙̙3f3fff3f3f33333f33333.lTU Delft Repositoryg Nuuidrepository linktitleauthorcontributorpublication yearabstract
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departmentresearch group programmeprojectcoordinates)uuid:f2de09647bf94232aae03b3fff3f953eDhttp://resolver.tudelft.nl/uuid:f2de09647bf94232aae03b3fff3f953eURigidbody kinematics versus flapping kinematics of a flapping wing micro air vehicleOCaetano, J.V.; Weehuizen, M.B.; De Visser, C.C.; De Croon, G.C.H.E.; Mulder, M.Several formulations have been proposed to model the dynamics of ornithopters, with inconclusive results regarding the need for complex kinematic formulations. Furthermore, the impact of assumptions made in the collected results was never assessed by comparing simulations with real flight data. In this study two dynamic models of a Flapping Wing Micro Aerial Vehicle (FWMAV) were derived and compared: a) single rigid body aircraft equations of motion and b) Virtual Work Principle derivation for multiple rigid body flapping kinematics. The aerodynamic forces and moments were compared by feeding the states that were reconstructed from the position and attitude data of a 17 gram free flying FWMAV into the dynamic equations of both formulations. To understand the applicability of rigid body formulations to FWMAVs, six wingtobody mass ratios and two wing configurations were studied using real flight data. The results show that rigid body models are valid for the aerodynamic reconstruction of FWMAVs with four wings in X configuration and twowinged FWMAV with a total wingtobody mass ratio below 24% and 5.6%, respectively, without considerable information loss.enjournal articleAIAAAerospace Engineering)uuid:9efe77a621e9404480c16a2467c7f579Dhttp://resolver.tudelft.nl/uuid:9efe77a621e9404480c16a2467c7f579sA highprecision positionbased calibration table as the reference for angular accelerometer calibration experiment?Jatiningrum, D.; De Visser, C.C.; Van Paassen, M.M.; Mulder, M.With the role of angular accelerometers in future faulttolerant flight control systems, an indepth evaluation of their performance then becomes a critical issue from the perspective of control system design. In this paper, a positionbased calibration table is utilized to provide a sufficiently accurate angular acceleration reference in the dynamic angular calibration. However, the angular accelerometer measured data contains a high noise level when transmitted through the slip rings. To tackle this issue, a customized sensor Data Acquisition System (DAS) is designed. It is mounted on the turntable top and has a direct access to the angular accelerometer data channel. To synchronize sensor and table data, two auxiliary signals are generated by the sensor DAS computer to help in the post measurement processing. The first signal is a regular pulse of 100 Hz, which is suitable to align sensor and table data. The second signal is a step function which acts as a data log trigger for the calibration table, as well as a marker of the record starting point. This approach results in a lower angular accelerometer noise level, below the specified limit of 3 mV. The ErrorRMS is 0:00195n, which after being calculated with the measurement results, evidently falls below the Gaussian probability density function specified by the standard of 5:672. As a result, the customized setup enables a commercially available calibration table to serve as the reference for angular accelerometer calibration experiments.conference paperControl & Operations)uuid:166e767c535e4a758b74f4ed4712c078Dhttp://resolver.tudelft.nl/uuid:166e767c535e4a758b74f4ed4712c078KNearHover Flapping Wing MAV Aerodynamic Modelling: A linear model approachiCaetano, J.V.; Verboom, J.; De Visser, C.C.; De Croon, G.C.H.E.; Remes, B.D.W.; De Wagter, C.; Mulder, M.)uuid:fa68d1b6623649b8819fc76452ddab3bDhttp://resolver.tudelft.nl/uuid:fa68d1b6623649b8819fc76452ddab3bdOnline Aerodynamic Model Identification using a Recursive Seq< uential Method for Multivariate Splines3Sun, L.G.; De Visser, C.C.; Chu, Q.P.; Mulder, J.A.6Avoiding high computational loads is essential to online aerodynamic model identi fication algorithms, which are at the heart of any modelbased adaptive flight control system. Multivariate simplex Bspline (MVSB) methods are excellent function approximation tools for modeling the nonlinear aerodynamics of high performance aircraft. However, the computational efficiency of the MVSB method must be improved in order to enable realtime onboard applications, for example in adaptive nonlinear flight control systems. In this paper, a new recursive sequential identification strategy is proposed for the MVSB method aimed at increasing its computational efficiency, thereby allowing its use in onboard system identification applications. The main contribution of this new method is a significant reduction of computational load for large scale online identification problems as compared to the existing MVSB methods. The proposed method consists of two sequential steps for each time interval, and makes use of a decomposition of the global problem domain into a number of subdomains, called modules. In the first step the Bcoefficients for each module are estimated using a least squares estimator. In the second step the local Bcoefficients for each module are then smoothened into a single global Bcoefficient vector using a linear minimum mean square errors (LMMSE) estimation. The new method is compared to existing batch and recursive MVSB methods in a numerical experiment in which an aerodynamic model is recursively identified based on data from an NASA F16 windtunnel model.Precursive identification; aerodynamic model identification; multivariate splines)uuid:e070de9de8054aa59bcc7f8719bb56e1Dhttp://resolver.tudelft.nl/uuid:e070de9de8054aa59bcc7f8719bb56e1eA novel adaptive kernel method with kernel centers determined by a support vector regression approach]The optimality of the kernel number and kernel centers plays a significant role in determining the approximation power of nearly all kernel methods. However, the process of choosing optimal kernels is always formulated as a global optimization task, which is hard to accomplish. Recently, an algorithm, namely improved recursive reduced least squares support vector regression (IRRLSSVR), was proposed for establishing a global nonparametric offline model, which demonstrates significant advantage in choosing representing and fewer support vectors compared with others. Inspired by the IRR LSSVR, a new adaptive parametric kernel method called WVLSSVR is proposed in this paper using the same type of kernels and the same centers as those used in the IRRLSSVR. Furthermore, inspired by the multikernel semiparametric support vector regression, the effect of the kernel extension is investigated in a recursive regression framework, and a recursive kernel method called GPKLSSVR is proposed using a compound type of kernels which are recommended for Gaussian process regression. Numerical experiments on benchmark data sets confirm the validity and effectiveness of the presented algorithms. The WVLSSVR algorithm shows higher approximation accuracy than the recursive parametric kernel method using the centers calculated by the kmeans clustering approach. The extended recursive kernel method (i.e. GPKLSSVR) has not shown advantage in terms of global approximation accuracy when validating the test data set without realtime updation, but it can increase modeling accuracy if the realtime identification is involved.Wsupport vector machine; recursive identification; adaptive model; kernel basis functionElsevier)uuid:fa6f33dbebf64ec09b1db87abdba1e23Dhttp://resolver.tudelft.nl/uuid:fa6f33dbebf64ec09b1db87abdba1e23`Validating the Multidimensional Spline Based Global Aerodynamic Model for the Cessna Citation IIDe Visser, C.C.; Mulder, J.A.gThe validation of aerodynamic models created using flight test data is a time consuming and often costly process. In this paper a new method for the validation of< global nonlinear aerodynamic models based on multivariate simplex splines is presented. This new method uses the unique properties of the multivariate simplex splines, a recent type of of multivariate spline, to speedup the process of aerodynamic model validation. Multivariate simplex splines are defined on nonrectangular domains and can be used to accurately fit scattered nonlinear datasets in any number of dimensions. The simplex splines consist of piecewise defined, ordinary multivariate polynomials with a predefined continuity between neighboring polynomial pieces. A recent method for nonlinear system identification based on multivariate simplex splines was used to create a global nonlinear aerodynamic model of the Cessna Citation II laboratory aircraft operated by the Delft University of Technology. In this paper, the multivariate spline based aerodynamic model for the pitching moment coefficient will be validated using both a model residual analysis as well as a statistical model quality analysis. It will be demonstrated that these new analysis methods, which are both unique to the multivariate simplex splines, provide a highly efficient and powerful new method for aerodynamic model validation.9American Institute of Aeronautics and Astronautics (AIAA))uuid:926233cbd99941f586a64ce19965dce5Dhttp://resolver.tudelft.nl/uuid:926233cbd99941f586a64ce19965dce5WDifferential constraints for bounded recursive identification with multivariate splines(De Visser, C.C.; Chu, Q.P.; Mulder, J.A.The ability to perform online model identification for nonlinear systems with unknown dynamics is essential to any adaptive modelbased control system. In this paper, a new differential equality constrained recursive least squares estimator for multivariate simplex splines is presented that is able to perform online model identification and bounded model extrapolation in the framework of a modelbased control system. A new type of linear constraints, the differential constraints, are used as differential boundary conditions within the recursive estimator which limit polynomial divergence when extrapolating data. The differential constraints are derived with a new, onestep matrix form of the de Casteljau algorithm, which reduces their formulation into a single matrix multiplication. The recursive estimator is demonstrated on a bivariate dataset, where it is shown to provide a speedup of two orders of magnitude over an ordinary least squares batch method. Additionally, it is demonstrated that inclusion of differential constraints in the least squares optimization scheme can prevent polynomial divergence close to edges of the model domain where local data coverage may be insufficient, a situation often encountered with global recursive data approximation.Rmultivariate splines; parameter estimation; scattered data; function approximators
20140501)uuid:045f136839644ac79626fbb47da8f277Dhttp://resolver.tudelft.nl/uuid:045f136839644ac79626fbb47da8f277]A Multidimensional Spline Based Global Nonlinear Aerodynamic Model for the Cessna Citation IIAA new method is proposed for the identification of global nonlinear models of aircraft nondimensional force and moment coefficients. The method is based on a recent type of multivariate spline, the multivariate simplex spline, which can accurately approximate very large, scattered nonlinear datasets in any number of dimensions. The new identification method is used to identify a global nonlinear aerodynamic model of high dimensionality for the Cessna Citation II laboratory aircraft operated by the Delft University of Technology and the Netherlands National Aerospace Laboratory. The data used in the identification process consisted of millions of measurements and was accumulated during more than 250 flight test maneuvers with the laboratory aircraft. The resulting models for the aerodynamic force and moment coefficients are continuous analytical functions as they consist of sets of piecewise defined, multivariate polynomials. The identified models were validated using a subset of the flight data, <with validation results showing a very close match between model and reality.)uuid:f2b6bdbd5d114ec5ac7ed2c2ce21144cDhttp://resolver.tudelft.nl/uuid:f2b6bdbd5d114ec5ac7ed2c2ce21144c=A new approach to linear regression with multivariate splinesA new methodology for creating highly accurate, static nonlinear maps from scattered, multivariate data is presented. This new methodology uses the Bform polynomials of multivariate simplex splines in a new linear regression scheme. This allows the use of standard parameter estimation techniques for estimating the Bcoefficients of the multivariate simplex splines. We present a generalized least squares estimator for the Bcoefficients, and show how the estimated Bcoefficient variances lead to a new model quality assessment measure in the form of the Bcoefficient variance surface. The new modeling methodology is demonstrated on a nonlinear scattered bivariate dataset.Csplines; parameter estimation; scattered data; multivariate splines
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