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Online Aerodynamic Model Identification using a Recursive Sequential Method for Multivariate SplinesSun, L.G. (author), De Visser, C.C. (author), Chu, Q.P. (author), Mulder, J.A. (author)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,...journal article 2013
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Sun, L.G. (author), De Visser, C.C. (author), Chu, Q.P. (author), Mulder, J.A. (author)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...journal article 2012
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De Visser, C.C. (author), Chu, Q.P. (author), Mulder, J.A. (author)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...journal article 2011
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De Visser, C.C. (author), Chu, Q.P. (author), Mulder, J.A. (author)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...journal article 2009