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Adaptive Variable Structure Control With Neuron for Path Tracking of Beaver AUV
- Source :
- IEEE Access, Vol 8, Pp 48566-48575 (2020)
- Publication Year :
- 2020
- Publisher :
- IEEE, 2020.
-
Abstract
- In order to improve path tracking performance of Beaver AUV, a new method for path tracking controller design is proposed basing on variable structure control theory and nonlinear dynamics model of Beaver. Switching surface of variable structure controllers is founded with corresponding error and error derivative of surge displacement and yaw angle. Exponential approach law is adopted to derive control algorithm for surge and yaw in the process of path tracking. Chattering phenomenon is restrained due to adaptive neurons which are used for approaching speed adjusting online. Simulation results of sinusoidal path tracking, circular path tracking and planning path tracking show that tracking precision of Beaver AUV is improved more evidently through variable structure control with adaptive approaching speed compared with variable structure control using fixed approaching speed. The method proposed in this article is effective and feasible.
- Subjects :
- 0209 industrial biotechnology
Variable structure control
General Computer Science
Computer science
02 engineering and technology
Tracking (particle physics)
Displacement (vector)
Vehicle dynamics
Computer Science::Robotics
symbols.namesake
020901 industrial engineering & automation
Control theory
0202 electrical engineering, electronic engineering, information engineering
Autonomous underwater vehicle
General Materials Science
variable structure control
020208 electrical & electronic engineering
General Engineering
Process (computing)
neuron
Euler angles
Variable (computer science)
Nonlinear system
path tracking
symbols
lcsh:Electrical engineering. Electronics. Nuclear engineering
lcsh:TK1-9971
Subjects
Details
- Language :
- English
- ISSN :
- 21693536
- Volume :
- 8
- Database :
- OpenAIRE
- Journal :
- IEEE Access
- Accession number :
- edsair.doi.dedup.....5f08c4d8db78609d8745dcaf93c83ecf