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- W2357146211 abstract "A new adaptive radial basis function neural network (RBFNN) sliding mode guidance law was proposed for intercepting maneuvering targets. First of all,we designed a sliding-surface using a quasi-parallel approach principle and variable structure control theory. We then used the sliding surface to input variables to the RBF neural network. In this case,the output was missile acceleration. In order to place the missile system on the sliding surface,we employed an adaptive algorithm that adjusts in real-time the connection weights of the RBF neural network. The missile acceleration in a given direction was determined by considering the target's acceleration as a disturbance,and thus it was not necessary to measure the target's acceleration directly. Therefore,this guidance law has strong robustness to target maneuvering. The new guidance law,which utilizes line-of-sight (LOS) measurement only,is simple to implement. Numerical simulations showed that the proposed guidance law yields better performance than proportional navigation." @default.
- W2357146211 created "2016-06-24" @default.
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- W2357146211 date "2009-01-01" @default.
- W2357146211 modified "2023-09-26" @default.
- W2357146211 title "A new adaptive RBFNN sliding mode guidance law for intercepting maneuvering targets" @default.
- W2357146211 hasPublicationYear "2009" @default.
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