Adaptive Neural Network Global Nonsingular Fast Terminal Sliding Mode Control for a Real Time Ground Simulation of Aerodynamic Heating Produced by Hypersonic Vehicles

Abstract

This paper presents a strategy for a thermal-structural test with quartz lamp heaters (TSTQLH), combined with an ultra-local model, a closed-loop controller, a linear extended state observer (LESO), and an auxiliary controller. The TSTQLH is a real time ground simulation of aerodynamic heating for hypersonic vehicles to optimize their thermal protection systems (TPS). However, lack of a system dynamic model for the TSTQLH results in inaccurate tracking of aerodynamic heating. In addition, during the control process, the TSTQLH has internal uncertainties of resistance and external disturbances. Therefore, it is necessary to establish a mathematical model between controllable α(t) and measurable T1(t). An ultra-local model of model-free control plays a crucial role in simplifying system complexity and reducing high-order terms due to high nonlinearities and strong couplings in the system dynamic model, and a global nonsingular fast terminal sliding mode control (GNFTSMC) is added to an ultra-local model, which is used to guarantee great tracking performance in the sliding phase and fast convergence to the equilibrium state in finite time. Moreover, the LESO is used mainly to estimate all disturbances in real time, and an adaptive neural network (ANN) shows a good approximation property in compensation for estimation errors by using a cubic B-spline function. The fitted curve of the wall temperature in the time sequence represents a reference temperature trajectory from the surface contour of an X-43A’s wing. The comparative results validate that the proposed control strategy possesses strong robustness to track the reference temperature trajectory.