Abstract
A new online identification method is presented. The identified nonlinear systems have partial-state measurement. Their inner states, parameters and structures are unknown. The design is based on the combination of a model-free state observer and a neuro identifier. First, a sliding mode observer, which does not need any information about the nonlinear system, is applied to obtain the full states. A dynamic multilayer neural network is then used to identify the whole nonlinear system. The main contributions of the paper are: a new observer-based identification algorithm is proposed; and a stable learning algorithm for the neuro identifier is given.
Original language | English |
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Pages (from-to) | 145-152 |
Number of pages | 8 |
Journal | IEE Proceedings: Control Theory and Applications |
Volume | 147 |
Issue number | 2 |
DOIs | |
State | Published - 2000 |
Externally published | Yes |