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Sliding Mode Control for NSVs with Input Constraint Using Neural Network and Disturbance Observer

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  • Yan-long Zhou
  • Mou Chen

Abstract

The sliding mode control (SMC) scheme is proposed for near space vehicles (NSVs) with strong nonlinearity, high coupling, parameter uncertainty, and unknown time-varying disturbance based on radial basis function neural networks (RBFNNs) and the nonlinear disturbance observer (NDO). Considering saturation characteristic of rudders, RBFNNs are constructed as a compensator to overcome the saturation nonlinearity. The stability of the closed-loop system is proved, and the tracking error as well as the disturbance observer error can converge to the origin through the Lyapunov analysis. Simulation results are presented to demonstrate the effectiveness of the proposed flight control scheme.

Suggested Citation

  • Yan-long Zhou & Mou Chen, 2013. "Sliding Mode Control for NSVs with Input Constraint Using Neural Network and Disturbance Observer," Mathematical Problems in Engineering, Hindawi, vol. 2013, pages 1-12, September.
    • Handle: RePEc:hin:jnlmpe:904830
    DOI: 10.1155/2013/904830
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