Nonlinear adaptive flight control using sliding mode online learning

An expanded model inversion flight control strategy using sliding mode online learning is presented and implemented for an small unmanned aircraft system (UAS). These low-cost aircraft are very susceptible towards nonlinearities like atmospheric turbulence, model uncertainties or system failures, so adaptive flight control strategies possess a high potential to improve the degree of automation of such systems. The concept of feedback linearization is combined with feedforward neural networks to compensate for inversion errors and other nonlinearities. The backpropagation-based adaption laws of the network weights are extended with terms to take into account any approximation error and the so called e-modification. Within these adaption laws the standard gradient descent backpropagation algorithm is expanded with the concept of sliding mode control (SMC), which enables stable adaptivity of the learning rate and so tends to offer a higher speed of convergence. The definition of the sliding mode function assures convergence of the network's output error towards the sliding surface, while considering the system's stability. The SMC-based flight control strategy is tested and compared with the standard gradient descent backpropagation algorithm.