Adaptive neural dynamic surface sliding mode control for uncertain nonlinear systems with unknown input saturation

Qiang Chen, Linlin Shi, Yurong Nan*, Xuemei Ren

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

11 Citations (Scopus)

Abstract

In this article, an adaptive neural dynamic surface sliding mode control scheme is proposed for uncertain nonlinear systems with unknown input saturation. The non-smooth input saturation nonlinearity is firstly approximated by a smooth non-affine function, which can be further transformed into an affine form according to the mean value theorem. Then, one simple sigmoid neural network is employed to approximate the uncertain nonlinearity including the input saturation, and the approximation error is estimated using an adaptive learning law. Virtual controls are designed in each step by combing the dynamic surface control and integral sliding mode technique, and thus the problem of complexity explosion inherent in the conventional backstepping method is avoided. With the proposed control scheme, no prior knowledge is required on the bound of input saturation, and comparative simulations are given to illustrate the effectiveness and superior performance.

Original languageEnglish
Pages (from-to)1-14
Number of pages14
JournalInternational Journal of Advanced Robotic Systems
Volume13
Issue number5
DOIs
Publication statusPublished - 21 Sept 2016

Keywords

  • Dynamic surface control
  • input saturation
  • integral sliding mode control
  • neural network
  • nonlinear system

Fingerprint

Dive into the research topics of 'Adaptive neural dynamic surface sliding mode control for uncertain nonlinear systems with unknown input saturation'. Together they form a unique fingerprint.

Cite this