Robust exponential stabilization of positive uncertain switched neural networks with actuator saturation and sensor faults
作者：
Highlights：
• A new yet general positive switched neural network model is established to fill the academic gap.
• The influence of sensor faults and actuator saturation on models is considered simultaneously. Besides, with interval uncertainties taken into consideration, a more comprehensive interval observer is constructed.
• The proposed method in this paper can be applied to the general nonlinear hybrid systems. Application of some novel techniques from the theory of nonlinear network cluster to positive uncertain switched systems yields a deep insight into the nonlinear dynamics under investigation.
摘要
•A new yet general positive switched neural network model is established to fill the academic gap.•The influence of sensor faults and actuator saturation on models is considered simultaneously. Besides, with interval uncertainties taken into consideration, a more comprehensive interval observer is constructed.•The proposed method in this paper can be applied to the general nonlinear hybrid systems. Application of some novel techniques from the theory of nonlinear network cluster to positive uncertain switched systems yields a deep insight into the nonlinear dynamics under investigation.
论文关键词：Positive switched neural networks,Descriptor state-bounding observer,Actuator saturation,Fault estimation,Time-varying linear co-positive Lyapunov function
论文评审过程：Received 22 November 2020, Revised 29 June 2021, Accepted 21 July 2021, Available online 1 August 2021, Version of Record 1 August 2021.
论文官网地址：https://doi.org/10.1016/j.amc.2021.126548
