Chebyshev Neural Network-Based Adaptive Nonsingular Terminal Sliding Mode Control for Hypersonic Vehicles

Ruimin Zhang, Qiaoyu Chen and Haigang Guo

Mathematical Problems in Engineering, 2020, vol. 2020, 1-10

Abstract:

This paper presents an adaptive nonsingular terminal sliding mode control approach for the attitude control of a hypersonic vehicle with parameter uncertainties and external disturbances based on Chebyshev neural networks (CNNs). First, a new nonsingular terminal sliding surface is proposed for a general uncertain nonlinear system. Then, a nonsingular sliding mode control is designed to achieve finite-time tracking control. Furthermore, to relax the requirement for the upper bound of the lumped uncertainty including parameter uncertainties and external disturbances, a CNN is used to estimate the lumped uncertainty. The network weights are updated by the adaptive law derived from the Lyapunov theorem. Meanwhile, a low-pass filter-based modification is added into the adaptive law to achieve fast and low-frequency adaptation when using high-gain learning rates. Finally, the proposed approach is applied to the attitude control of the hypersonic vehicle and simulation results illustrate its effectiveness.

Date: 2020
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Persistent link: https://EconPapers.repec.org/RePEc:hin:jnlmpe:6830141

DOI: 10.1155/2020/6830141

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