Abstract
The paper proposes a finite series expansion to approximate general nonlinear dynamic models to arbitrary accuracy. The method produces an approximation of nonlinear dynamics in the form of an aggregation of linear models, weighted by unimodal basis functions, and results in a linear growth bound on the approximation error. Furthermore, the paper demonstrates that the proposed approximation satisfies the modeling assumptions for analysis based on linear matrix inequalities and hence widens the applicability of these techniques to the area of nonlinear control.
Reference14 articles.
1. R. Liu, R. Saeks, and R. J. Leake, “On global linearization,” in SIAM-AMS, 1969, pp. 93–102.
2. Wang
L. X.
and MendelJ. M., “Fuzzy basis functions, universal approximators and orthogonal least-squares learning,” IEEE Transactions on Neural Networks, vol. 3, pp. 807–814, 1992.
3. Chen
S.
, CowanC. F. N., and GrantP. M., “Orthogonal least squares learning algorithms for radial basis function networks,” IEEE Transactions on Neural Networks, vol. 2, no. 2, pp. 302–309, 1991.
4. Chen
S.
, BillingsS. A., and LuoW., “Orthogonal least squares methods and their application to non-linear system identification,” International Journal of Control, vol. 50, no. 5, pp. 1873–1896, 1989.
5. Leontaritis
I. J.
and BillingsS. A., “Input-output parametric models for non-linear systems, Part I: deterministic non-linear systems,” International Journal of Control, vol. 41, no. 2, pp. 303–328, 1985.
Cited by
2 articles.
订阅此论文施引文献
订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献