Kinematic Prediction for Intercept Using a Neural Kalman Filter
نویسندگان
چکیده
The neural extended Kalman filter is a technique that learns unmodelled dynamics while performing state estimation. This coupled system performs the state estimation of the plant while estimating a function approximation of the difference between the system model and the dynamics of the true plant. At each sample step, this approximation is added to the existing model improving the state estimate. This neural estimator is applied to a two-dimensional intercept problem as a target tracker providing the control reference signal. Comparisons between different prediction times used in the control are provided for both the neural tracker and a baseline tracker. Copyright © 2005 IFAC
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