Robust approximate pole assignment for second-order systems : Neural network computation
Research output: Journal Publications and Reviews (RGC: 21, 22, 62) › 21_Publication in refereed journal › peer-review
Author(s)
Related Research Unit(s)
Detail(s)
| Original language | English |
|---|---|
| Pages (from-to) | 923-928 |
| Journal / Publication | Journal of Guidance, Control, and Dynamics |
| Volume | 21 |
| Issue number | 6 |
| Publication status | Published - Nov 1998 |
Link(s)
Abstract
A recurrent neural network approach to robust approximate pole assignment for second-order systems is proposed. The design is formulated as an unconstrained optimization problem and solved via the gradient-flow approach, which is ideally suited for neural network implementation. Convergence of the gradient flow also is established. Simulation results are used to demonstrate the effectiveness of the proposed method.
Citation Format(s)
Robust approximate pole assignment for second-order systems : Neural network computation. / Ho, Daniel W. C.; Lam, James; Xu, Jinhua.
In: Journal of Guidance, Control, and Dynamics, Vol. 21, No. 6, 11.1998, p. 923-928.Research output: Journal Publications and Reviews (RGC: 21, 22, 62) › 21_Publication in refereed journal › peer-review
