Event-based adaptive NN controller design for strict-feedback discrete-time nonlinear systems with input dead zone and saturation

Abstract

In this paper, an event-based adaptive neural network controller design method is proposed for a type of uncertain strict-feedback discrete-time nonlinear systems. This system contains uncertain functions and has input nonlinearities in the form of saturation and non-symmetric dead zone. Both event-triggered policy and adaptive law are considered. Radial basis function neural networks are employed to accomplish function approximation. Input dead zone and saturation are estimated by a summation of a known affine function and a bounded unknown function. A stabilising controller and adaptive law are designed via backstepping. The stability of the controlled systems is elaborated via the difference Lyapunov analysis method. Simulation results are given to verify the effectiveness of the proposed design scheme.

Keywords:

• Nonlinear discrete-time system
• dead zone
• saturation
• backstepping
• event-triggered

Disclosure statement

No potential conflict of interest was reported by the authors.

Additional information

Funding

This work was supported by National Natural Science Foundation of China [grant number 61773072], The Natural Sciences and Engineering Research Council of Canada [grant number 2017-05637] and Taishan Scholar Project of Shandong Province [grant number 2015162 and tsqn201812093].