"uuid","repository link","title","author","contributor","publication year","abstract","subject topic","language","publication type","publisher","isbn","issn","patent","patent status","bibliographic note","access restriction","embargo date","faculty","department","research group","programme","project","coordinates"
"uuid:f2de0964-7bf9-4232-aae0-3b3fff3f953e","http://resolver.tudelft.nl/uuid:f2de0964-7bf9-4232-aae0-3b3fff3f953e","Rigid-body kinematics versus flapping kinematics of a flapping wing micro air vehicle","Caetano, J.V.; Weehuizen, M.B.; De Visser, C.C.; De Croon, G.C.H.E.; Mulder, M.","","2015","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 wing-to-body 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 two-winged FWMAV with a total wing-to-body mass ratio below 24% and 5.6%, respectively, without considerable information loss.","","en","journal article","AIAA","","","","","","","","Aerospace Engineering","","","","",""
"uuid:9efe77a6-21e9-4044-80c1-6a2467c7f579","http://resolver.tudelft.nl/uuid:9efe77a6-21e9-4044-80c1-6a2467c7f579","A high-precision position-based calibration table as the reference for angular accelerometer calibration experiment","Jatiningrum, D.; De Visser, C.C.; Van Paassen, M.M.; Mulder, M.","","2015","With the role of angular accelerometers in future fault-tolerant flight control systems, an in-depth evaluation of their performance then becomes a critical issue from the perspective of control system design. In this paper, a position-based 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 turn-table 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.","","en","conference paper","","","","","","","","","Aerospace Engineering","Control & Operations","","","",""
"uuid:166e767c-535e-4a75-8b74-f4ed4712c078","http://resolver.tudelft.nl/uuid:166e767c-535e-4a75-8b74-f4ed4712c078","Near-Hover Flapping Wing MAV Aerodynamic Modelling: A linear model approach","Caetano, J.V.; Verboom, J.; De Visser, C.C.; De Croon, G.C.H.E.; Remes, B.D.W.; De Wagter, C.; Mulder, M.","","2013","","","en","conference paper","","","","","","","","","Aerospace Engineering","Control & Operations","","","",""
"uuid:fa68d1b6-6236-49b8-819f-c76452ddab3b","http://resolver.tudelft.nl/uuid:fa68d1b6-6236-49b8-819f-c76452ddab3b","Online Aerodynamic Model Identification using a Recursive Sequential Method for Multivariate Splines","Sun, L.G.; De Visser, C.C.; Chu, Q.P.; Mulder, J.A.","","2013","Avoiding high computational loads is essential to online aerodynamic model identi- fication algorithms, which are at the heart of any model-based adaptive flight control system. Multivariate simplex B-spline (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 real-time 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 B-coefficients for each module are estimated using a least squares estimator. In the second step the local B-coefficients for each module are then smoothened into a single global B-coefficient 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 F-16 wind-tunnel model.","recursive identification; aerodynamic model identification; multivariate splines","en","journal article","AIAA","","","","","","","","Aerospace Engineering","Control & Operations","","","",""
"uuid:e070de9d-e805-4aa5-9bcc-7f8719bb56e1","http://resolver.tudelft.nl/uuid:e070de9d-e805-4aa5-9bcc-7f8719bb56e1","A novel adaptive kernel method with kernel centers determined by a support vector regression approach","Sun, L.G.; De Visser, C.C.; Chu, Q.P.; Mulder, J.A.","","2012","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 (IRR-LSSVR), 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 WV-LSSVR is proposed in this paper using the same type of kernels and the same centers as those used in the IRR-LSSVR. 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 GPK-LSSVR 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 WV-LSSVR algorithm shows higher approximation accuracy than the recursive parametric kernel method using the centers calculated by the k-means clustering approach. The extended recursive kernel method (i.e. GPK-LSSVR) has not shown advantage in terms of global approximation accuracy when validating the test data set without real-time updation, but it can increase modeling accuracy if the real-time identification is involved.","support vector machine; recursive identification; adaptive model; kernel basis function","en","journal article","Elsevier","","","","","","","","Aerospace Engineering","Control & Operations","","","",""
"uuid:fa6f33db-ebf6-4ec0-9b1d-b87abdba1e23","http://resolver.tudelft.nl/uuid:fa6f33db-ebf6-4ec0-9b1d-b87abdba1e23","Validating the Multidimensional Spline Based Global Aerodynamic Model for the Cessna Citation II","De Visser, C.C.; Mulder, J.A.","","2011","The 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 non-rectangular 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.","","en","conference paper","American Institute of Aeronautics and Astronautics (AIAA)","","","","","","","","Aerospace Engineering","Control & Operations","","","",""
"uuid:926233cb-d999-41f5-86a6-4ce19965dce5","http://resolver.tudelft.nl/uuid:926233cb-d999-41f5-86a6-4ce19965dce5","Differential constraints for bounded recursive identification with multivariate splines","De Visser, C.C.; Chu, Q.P.; Mulder, J.A.","","2011","The ability to perform online model identification for nonlinear systems with unknown dynamics is essential to any adaptive model-based 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 model-based 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, one-step 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.","multivariate splines; parameter estimation; scattered data; function approximators","en","journal article","Elsevier","","","","","","","2014-05-01","Aerospace Engineering","Control & Operations","","","",""
"uuid:045f1368-3964-4ac7-9626-fbb47da8f277","http://resolver.tudelft.nl/uuid:045f1368-3964-4ac7-9626-fbb47da8f277","A Multidimensional Spline Based Global Nonlinear Aerodynamic Model for the Cessna Citation II","De Visser, C.C.; Mulder, J.A.","","2010","A new method is proposed for the identification of global nonlinear models of aircraft non-dimensional 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.","","en","conference paper","American Institute of Aeronautics and Astronautics (AIAA)","","","","","","","","Aerospace Engineering","Control & Operations","","","",""
"uuid:f2b6bdbd-5d11-4ec5-ac7e-d2c2ce21144c","http://resolver.tudelft.nl/uuid:f2b6bdbd-5d11-4ec5-ac7e-d2c2ce21144c","A new approach to linear regression with multivariate splines","De Visser, C.C.; Chu, Q.P.; Mulder, J.A.","","2009","A new methodology for creating highly accurate, static nonlinear maps from scattered, multivariate data is presented. This new methodology uses the B-form polynomials of multivariate simplex splines in a new linear regression scheme. This allows the use of standard parameter estimation techniques for estimating the B-coefficients of the multivariate simplex splines. We present a generalized least squares estimator for the B-coefficients, and show how the estimated B-coefficient variances lead to a new model quality assessment measure in the form of the B-coefficient variance surface. The new modeling methodology is demonstrated on a nonlinear scattered bivariate dataset.","splines; parameter estimation; scattered data; multivariate splines","en","journal article","Elsevier","","","","","","","2014-05-01","Aerospace Engineering","Control & Operations","","","",""