Robust neural tracking control for switched nonaffine stochastic nonlinear systems with unknown control directions and backlash-like hysteresis

Abstract This paper is concerned with the tracking control problem for a class of switched stochastic nonlinear systems in nonaffine form with both unknown control directions and unknown backlash-like hysteresis, and a novel neural tracking control scheme is proposed based on backstepping technique and Nussbaum function. Dynamic surface control (DSC) is adopted to overcome the problem of complexity explosion of the traditional backstepping design. High-order neural networks (HONNs) are utilized to approximate the lumped unknown nonlinear functions, and only one adaptive parameter needs to be updated. Stability analysis shows all closed-loop error signals are semi-globally uniformly ultimately bounded in the fourth-moment (or mean square), and the system tracking error is ensured to converge to a small neighborhood of zero. Finally, simulation results illustrate the effectiveness of the proposed scheme.

• Publication year

  2020

• Venue

  Journal of the Franklin Institute

• Publication date

  2020-03-01

• Fields of study

  Computer Science, Engineering

• Identifiers
  DOI 10.1016/j.jfranklin.2019.12.011
• External record

  Open on Semantic Scholar

• Source metadata

  Semantic Scholar

• No claims are published for this paper.

• No concepts are published for this paper.

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