A neural network baseline problem for control of aircraft flare and touchdown
Neural networks for control
Learning internal representations by error propagation
Parallel distributed processing: explorations in the microstructure of cognition, vol. 1
Genetic algorithms + data structures = evolution programs (2nd, extended ed.)
Genetic algorithms + data structures = evolution programs (2nd, extended ed.)
Genetic design of rule-based fuzzy controllers
Genetic design of rule-based fuzzy controllers
Practical genetic algorithms
A learning algorithm for continually running fully recurrent neural networks
Neural Computation
Application of Neural Networks to Disturbances Encountered Landing Control
IEEE Transactions on Intelligent Transportation Systems
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
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This paper presents an intelligent aircraft automatic landing controller that uses recurrent neural networks (RNN) with genetic algorithms (GAs) to improve the performance of conventional automatic landing system (ALS) and guide the aircraft to a safe landing. Real-time recurrent learning (RTRL) is applied to train the RNN that uses gradient-descent of the error function with respect to the weights to perform the weights updates. Convergence analysis of system error is provided. The control scheme utilizes five crossover methods of GAs to search optimal control parameters. Simulations show that the proposed intelligent controller has better performance than the conventional controller.