Discrete-time high-order neural network identifier trained with high-order sliding mode observer and unscented Kalman filter

This paper presents a method to identify an unknown discrete-time nonlinear system, using high-order neural networks and high-order sliding mode algorithms, which may be subject to internal and external disturbances. Based on the information obtained from available system states, a high-order neural network model is proposed to approximate the system dynamics. Neural network weights are trained by means of the unscented Kalman filter and high-order sliding mode observer. A simulation example is included to illustrate effectiveness of the proposed scheme. (c) 2019 Elsevier B.V. All rights reserved.

Automated summary

This paper introduces a method for identifying unknown discrete-time nonlinear systems using high-order neural networks and high-order sliding mode algorithms. The neural network model approximates system dynamics based on available system state information, and neural network weights are trained using the unscented Kalman filter and high-order sliding mode observer. A simulation example is provided to demonstrate the effectiveness of the proposed scheme.