A robust neuro-control scheme is presented for aircraft auto-landing under severe wind conditions and partial loss of control surfaces. In the scheme, a dynamic radial basis function network (RBFN) called minimal resource allocating network (MRAN), that incorporates a growing and pruning strategy, is utilize to aid an H/sub /spl infin// controller using a feedback-error-learning mechanism. The neural network uses only online learning and is not trained "a priori". Specifically, the performance of this neuro-controller for aircraft auto-landing in a microburst along with a partial loss of control effectiveness is analyzed and compared with other control schemes. Simulation studies show that the performance obtained by the neuro-H/sub /spl infin// control scheme is better than the other control schemes under failure and extreme wind conditions.
Robust neuro-H/sub /spl infin// controller design for aircraft auto-landing
IEEE Transactions on Aerospace and Electronic Systems ; 40 , 1 ; 158-167
01.01.2004
1088336 byte
Aufsatz (Zeitschrift)
Elektronische Ressource
Englisch
Robust Neuro-H(infinity) Controller Design for Aircraft Auto-Landing
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