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

Abstract EN

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.