By L Dugard; M MʼSaad; Yoan D Landau; International Federation of Automatic Control
Adaptive platforms stay a really attention-grabbing box of theoretical learn, prolonged by way of methodological reviews and a growing number of purposes. The plenary papers, invited classes and contributed classes excited about many elements of adaptive structures, corresponding to structures id and modelling, adaptive keep watch over of nonlinear structures and theoretical matters in adaptive keep watch over. additionally lined have been methodological elements and functions of adaptive regulate, clever tuning and adaptive sign processing.
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Additional resources for Adaptive systems in control and signal processing 1992 : selected papers from the fourth IFAC symposium, Grenoble, France, 1-3 July 1992
In addition, the plots in Fig. 3 show the desired properties of long phases with constant para meters and robustness against minor excitation. Espe cially the dashed line of h is a good example for constant phases during lack of excitation and quick convergence to wards the " correct" value in the presence of excitation. 6 deg/s > r1 . The remaining three plots show the time series of the identified parameters ft , h , R, respectively. The solid line is taken from the original record. The dashed line was computed off-line with the same algorithm but starting with a model 0 from a " far edge" of the possible parameter space.
47-52. [ 1 2) R. E. Kalman and J. E. Bertram, Co11trol System A nalysis and Desig11 Via the "seco11d Method" of Lyap1111ov (I Continuous-Time Systems), Journal of Basic Engineering, Trans. of the ASWE. June 1 960. 37 1 - 391. 34 Copyright © IFAC Adaptive Systems in Control and Signal Processing, Grenoble, France, 1992 TOWARDS REAL-TIME IMPLEMENTATION OF BAYESIAN PARAMETER ESTIMATION R. Kulhavy• ,t, I. Nagy** and J. O. O. Box 18, 182 08 Prague, Czechoslovakia Abstract. The paper describes a recent progress in searching for credible, well-grounded approxima tion of recursive Bayesian parameter estimation which would make the Bayesian paradigm feasible for a class of nonstandard (non-linear and/or non-Gaussian) models.
The actual parameter vector 0o deter mines the candidate models with changes of a single para meter R, Ji , h , b (eight neighbors) and "diagonal" changes of two parameters R, Ji (four neighbors) and R, h (four neighbors) . This seems to be sufficient for the representa tion of all directions in the parameter space. The equations are readily transformed to discrete time: T1+1 Tt+ l • Neighboring Models. (24) turnrate rudder angle (actuating signal) velocity of the ship constant coefficient of rudder momentum nonlinear hydrodynamic momentum asymmetric momentum caused by wind, single screw action, etc.