Adaptive Backstepping-Based Neural Network Control for Hypersonic Reentry Vehicle With Input Constraints

In this paper, the attitude tracking control problem of hypersonic reentry vehicles is addressed by synthesizing a neural network (NN) using the backstepping control technique. The control-oriented model is formulated with mismatched and matched lumped uncertainties, which reflect the multiple aerod...

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Bibliographic Details
Published in:IEEE access 2018-01, Vol.6, p.1954-1966
Main Authors: Ma, Guangfu, Chen, Chen, Lyu, Yueyong, Guo, Yanning
Format: Article
Language:English
Subjects:
Actuators
Adaptive control
Aerodynamics
Artificial neural networks
Attitude control
Backstepping
Constraints
disturbance observer
Disturbance observers
Heart rate variability
HRV
Hypersonic reentry
input constraints
Network control
Neural network
Neural networks
Radial basis function
Reentry
Reentry vehicles
Saturation
Tracking control
Tracking errors
Uncertainty
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Summary:In this paper, the attitude tracking control problem of hypersonic reentry vehicles is addressed by synthesizing a neural network (NN) using the backstepping control technique. The control-oriented model is formulated with mismatched and matched lumped uncertainties, which reflect the multiple aerodynamic uncertainties, external disturbances, and actuator saturation. Based on the universal approximation property of the radial basis function NN, an adaptive NN disturbance observer is developed to estimate the lumped disturbances online using only the tracking error state as its input vector. The "explosion of terms" problem in backstepping is avoided using a tracking differentiator. To address the input constraints, a sigmoid function is introduced to approximate the saturation and guarantee that the control input is bounded. In particular, a novel auxiliary system, driven by the tracking error and the input error between the unconstrained input and the constrained input, which was processed using the sigmoid function, is further designed to reduce the saturation effects and satisfy the stability requirement. Via modification of the adaptive laws, the tracking errors are guaranteed to be uniformly ultimately bounded based on Lyapunov theory. Moreover, several simulations are investigated to show the effectiveness of the proposed control scheme.
ISSN:2169-3536
2169-3536
DOI:10.1109/ACCESS.2017.2780994