Flight control design using robust dynamic inversion and time-scale separation
Automatica (Journal of IFAC)
Bivariate Splines of Various Degrees for Numerical Solution of Partial Differential Equations
SIAM Journal on Scientific Computing
Discrete & Computational Geometry
Brief paper: A new approach to linear regression with multivariate splines
Automatica (Journal of IFAC)
High-Quality Rendering of Varying Isosurfaces with Cubic Trivariate C1-Continuous Splines
ISVC '09 Proceedings of the 5th International Symposium on Advances in Visual Computing: Part I
Recursive Bayesian recurrent neural networks for time-series modeling
IEEE Transactions on Neural Networks
Paper: A theoretical analysis of recursive identification methods
Automatica (Journal of IFAC)
Improved estimation performance using known linear constraints
Automatica (Journal of IFAC)
Nonlinear system identification via direct weight optimization
Automatica (Journal of IFAC)
Delaunay refinement algorithms for triangular mesh generation
Computational Geometry: Theory and Applications
| Hi-index | 22.14 |
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.