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DOB-Based Neural Control of Flexible Hypersonic Flight Vehicle Considering Wind Effects
DOB-Based Neural Control of Flexible Hypersonic Flight Vehicle Considering Wind Effects
- Source :
- IEEE Transactions on Industrial Electronics. 64:8676-8685
- Publication Year :
- 2017
- Publisher :
- Institute of Electrical and Electronics Engineers (IEEE), 2017.
-
Abstract
- 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.
- Subjects :
- 0209 industrial biotechnology
Engineering
Adaptive control
Artificial neural network
business.industry
Hypersonic flight
Control engineering
02 engineering and technology
Aerodynamics
Electronic mail
Nonlinear system
020901 industrial engineering & automation
Control and Systems Engineering
Control theory
Convergence (routing)
0202 electrical engineering, electronic engineering, information engineering
020201 artificial intelligence & image processing
Electrical and Electronic Engineering
business
Subjects
Details
- ISSN :
- 15579948 and 02780046
- Volume :
- 64
- Database :
- OpenAIRE
- Journal :
- IEEE Transactions on Industrial Electronics
- Accession number :
- edsair.doi...........c8fc40e24c1b005381841f875e8fdd3c
- Full Text :
- https://doi.org/10.1109/tie.2017.2703678