Multilayer feedforward networks are universal approximators
Neural Networks
Neural networks: a systematic introduction
Neural networks: a systematic introduction
Sliding mode algorithm for online learning in analog multilayer feedforward neural networks
ICANN/ICONIP'03 Proceedings of the 2003 joint international conference on Artificial neural networks and neural information processing
Output feedback control of a quadrotor UAV using neural networks
IEEE Transactions on Neural Networks
Online learning in adaptive neurocontrol schemes with a slidingmode algorithm
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Multilayer neural-net robot controller with guaranteed tracking performance
IEEE Transactions on Neural Networks
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An expanded nonlinear model inversion flight control strategy using sliding mode online learning for neural networks is presented. The proposed control strategy is implemented for a small unmanned aircraft system (UAS). This class of aircraft is very susceptible towards nonlinearities like atmospheric turbulence, model uncertainties and of course system failures. Therefore, these systems mark a sensible testbed to evaluate fault-tolerant, adaptive flight control strategies. Within this work the concept of feedback linearization is combined with feed forward neural networks to compensate for inversion errors and other nonlinear effects. Backpropagation-based adaption laws of the network weights are used for online training. Within these adaption laws the standard gradient descent backpropagation algorithm is augmented with the concept of sliding mode control (SMC). Implemented as a learning algorithm, this nonlinear control strategy treats the neural network as a controlled system and allows a stable, dynamic calculation of the learning rates. While considering the system's stability, this robust online learning method therefore offers a higher speed of convergence, especially in the presence of external disturbances. The SMC-based flight controller is tested and compared with the standard gradient descent backpropagation algorithm in the presence of system failures.