Title :
Nonlinear uncertainty model unfalsification
Author :
Kosut, Robert L.
Author_Institution :
SC Solutions, Santa Clara, CA, USA
Abstract :
Uncertainty model unfalsification is discussed. In particular, it is shown how to establish a trade-off between disturbance and possibly nonlinear time-invariant dynamic uncertainty using standard prediction error modeling. Parameters defining the central model and bounds on the RMS value of the disturbance and gain of the nonlinear dynamical error can be calculated directly from finite input-output data
Keywords :
identification; nonlinear systems; optimal control; optimisation; robust control; uncertain systems; identification; nonlinear dynamical error; nonlinear systems; optimisation; prediction error modeling; robust control; uncertain systems; uncertainty model unfalsification; Automatic control; Control systems; Error correction; Humans; Machine learning; Mathematical model; Predictive models; Robust control; System identification; Uncertainty;
Conference_Titel :
American Control Conference, 1997. Proceedings of the 1997
Conference_Location :
Albuquerque, NM
Print_ISBN :
0-7803-3832-4
DOI :
10.1109/ACC.1997.611060
