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By L Dugard; M MʼSaad; Yoan D Landau; International Federation of Automatic Control

Adaptive platforms stay a really fascinating box of theoretical study, prolonged via methodological reviews and progressively more purposes. The plenary papers, invited periods and contributed periods fascinated about many points of adaptive structures, corresponding to structures id and modelling, adaptive regulate of nonlinear platforms and theoretical concerns in adaptive regulate. additionally lined have been methodological elements and purposes of adaptive keep watch over, clever tuning and adaptive sign processing.

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Read or Download Adaptive systems in control and signal processing 1992 : selected papers from the fourth IFAC symposium, Grenoble, France, 1-3 July 1992 PDF

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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

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Rt gives a full description of data history at the time t. 's where the weight on R; tends to 1 with the increasing number of observations t while the weight on Ro goes at the same time to zero [see Fer­ guson (1973) for a role of (9) in non-parametric Bayesian estimation]. Yt and some known vector function of previous data Zt . In control applications, Zt is usually formed by a vector of delayed outputs Yt-i and inputs Ut -j or their filtered values. Incomplete knowledge of the relationship is ex­ pressed by parametrizing (1) with a vector 0 E 8 .

Robust control de­ sign circumventing linearization. A utomatica, in prepa­ ration. N. (1961). The problem of the most rapid de­ tection of a disturbance in a stationary process. Soviet Math. Dok/. 2, 795-799. Wahlberg, B. and L. Ljung ( 1991) . On estimation of trans­ fer function error bounds. 2. Hermes, Paris. pp. 1378-1383. Wittenmark, B. -0. llen (1991) . Identification and design for robust adaptive control. 2. Hermes, Paris. pp. 1390-1395. -0. Gutman and L. Neumann (1986). An algorithm for adaption of a robust controller to reduced plant uncertainty.

The full circle stands for the present model e0 and the arrows indicate the eight neighboring can­ didate models. The figure shows the important decrease of computational complexity when only neighboring models have to be tested (8 candidates instead of 62). This benefit of the linearity-in-the-parameters is even more significant in higher-dimensional parameter spaces. Hence, the Hinkley-detector with time-varying jump mag­ nitude 2pt = m(ut , e1 ) - m(ut , eo ) is applicable. The jump magnitude Pt may be positive or negative as well.

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