Fault-tolerant nonlinear adaptive flight control using sliding mode online learning
- 作者：Thomas Krü ; ger ; thomas.krueger@tu-bs.de ; Philipp Schnetter p.schnetter@tu-bs.de ; Robin Placzek r.placzek@tu-bs.de ; Peter Vö ; rsmann p.voersmann@tu-bs.de
- 关键词：Adaptive flight control ; Sliding mode online learning ; Variable learning rate ; Unmanned aircraft system
- 刊名：Neural Networks
- 出版年：2012
- 期刊代码：94_08936080
- 类别：cp
- 出版时间：August, 2012
- 卷：32
- 期：Complete
- 页码：267-274
- 文件大小：669 K
摘要
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鈥檚 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.