Deterministic learning and nonlinear observer design
A " deterministic learning " (DL) theory was recently proposed for identification of nonlinear system dynamics under full-state measurements. In this paper, for a class of nonlinear systems undergoing periodic or recurrent motions with only output measurements, firstly, it is shown that locally-accurate identification of nonlinear system dynamics can still be achieved. Specifically, by using a high gain observer and a dynamical radial basis function network (RBFN), when state estimation is...[Show more]
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|Source:||Asian Journal of Control|
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