In many practical systems there is a delay in some of the sensor devices, for instance vision measurements that m a y have a long processing time. How to fuse these measurements in a Kalman filter is not a trivial problem if the computational delay is critical. Depending on how much time there is at hand, the designer has to make trade offs between optimality and computational burden of the filter. In this paper various methods in the literature along with a new method proposed by the authors will be presented and compared. The nem method is based on "extrapolaiing" the measurem.ent to present time using past and present estimates of the Kalman filter and calculating an optimal gain for this extrapolated m.easurement.
E m a i l tdlOiau.dtn.dk mX: +45 to make an accurate dynamical model of the robot contemplating all the nonlinearities caused by for instance friction forces, is not a trivial task and is hardly ever seen in the literature (one example though is found in[l]). The problem (besides the noulinearities) is that a lot of parameters that change with for instance time and temperature are required to be known quite precisely.
To assist the identification of nonlinear dynamic systems, a set of tools has been developed for the MATLAB" environment. The tools include a number of different model structures, highly effective training algorithms, functions for validating trained networks, and pruning algorithms for determination of optimal network architectures. The toolbox should be regarded as a nonlinear extension to the System Identification Toolbox provided by The MathWorks, Inc [9]. This paper gives a brief overview of the entire collection of toolbox functions.
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