Neural network observer based on fuzzy auxiliary sliding-mode-control for nonlinear systems

Abstract

This research suggests the use of neural network observers based on fuzzy auxiliary sliding mode control for the fault estimation and isolation of quadrotor unmanned aerial vehicle sensors. The fuzzy auxiliary sliding-mode-control-based adaptive approach for neural network observer is used for the approximation, reconstruction, and isolation of unknown faults by utilizing the multi-layer neural network. The neural network weight parameters are updated adaptively by using the fuzzy auxiliary sliding-mode-control approach. In conventional neural networks, the gradient descent approach-based back-propagation procedures are adapted for the training of the neural network. In this research, a new concept of the nonlinear controller such as the fuzzy auxiliary sliding-mode-control method is adapted for neural network online training, in which the fuzzy auxiliary sliding-mode-control is used as the learning approach; the neural network is used as a control process that calculates the stable and dynamic learning rates. By the consideration of unknown faults diagnosis and isolation, the online learning approach used in this research has shown the faults estimation, reconstruction, and isolation abruptly and with high accuracy compared to conventional approaches as well the approach used in literature. Strategies adopted in the literature are not capable of fault detection, estimation, and reconstruction with high accuracy and abruptness compared to the method adopted in this research. The presented approach is validated by using the nonlinear dynamics of quadrotor unmanned aerial vehicles, results show the accuracy, abruptness, and efficiency compared to the algorithms adapted in the literature. It is suggested that the proposed strategy can be integrated into nonlinear systems fault diagnosis, fault isolation, and for increasing the system performance.