A three dimensional robust guidance law design based on RBF neural network gain adjustment
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Abstract
By adopting the three dimensional nonlinear model for the relative motion of missiles and targets,a scheme of guidance law was presented. The theoretical basis of the guidance law includes input-to-state stability (ISS) as well as the dynamic adjustment and self-study ability of the radial basis function (RBF) neural network. The control law is capable of dynamically adjusting the gain of nonlinear guidance law with the angular rate change of LOS (line of sight). The guidance law can avoid the undershoot augment caused by gain fixation and large-scale target-maneuvering, and also effectively trace as well as intercept the target making a variety of maneuvers. The numerical simulation results demonstrate the adaptivity and easy implementation of the control law.
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