DOB-Based Neural Control of Flexible Hypersonic Flight Vehicle Considering Wind Effects

• Published in: IEEE transactions on industrial electronics (1982) Vol. 64; no. 11; pp. 8676 - 8685
• Main Authors: Xu, Bin, Wang, Danwei, Zhang, Youmin, Shi, Zhongke
• Format: Journal Article
• Language: English
• Published: New York IEEE 01.11.2017; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Accessional attack angle; Adaptation models; Adaptive control; Aerodynamics; Angle of attack; Approximation; Artificial neural networks; Compounds; Computer simulation; Control systems design; disturbance observer (DOB); Disturbance observers; Electronic mail; Estimation; flexible hypersonic flight dynamics; Flight vehicles; Fuel consumption; Hypersonic flight; Mathematical analysis; Neural networks; neural networks (NNs); Rigid structures; Uncertainty; Weight; wind effect; Wind effects
• DOI: 10.1109/TIE.2017.2703678
• ISSN: 0278-0046

This paper investigates the disturbance observer (DOB)-based neural adaptive control on the longitudinal dynamics of a flexible hypersonic flight vehicle (HFV) in the presence of wind effects. The coupling effect between flexible states and rigid body, and the accessional angle of attack (AOA) due to wind, is modeled as unknown disturbance, where the nonlinear DOB is constructed using the neural approximation. For the weight update in neural networks (NNs), a novel algorithm is proposed with the additional prediction error derived from the serial-parallel estimation model (SPEM) using both neural approximation and disturbance estimation. Different from previous work, the wind effect is taken into the hypersonic flight dynamics for realistic analysis, and the novel controller is designed using compound estimation, where the NN and the DOB are constructed to deal with aerodynamic uncertainty and unknown disturbance. Simulation studies of a flexible HFV with wind effects show that the proposed controller can achieve high tracking accuracy, while the compound estimation can closely follow the system uncertainty with fast convergence.