Neural Network Predictive Control (NNPC) of a Deep Submergence Rescue Vehicle (DSRV)
In this paper, the modeling and design of the depth control systems using Neural Network Predictive Control (NNPC)for a small unmanned underwater vehicle (UUV) will be described. Underwater vehicles consist of robotic vehicles that have been developed to reduce the risks of human life and to carry...
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| Main Authors: | , , , |
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| Format: | Conference or Workshop Item |
| Language: | en |
| Published: |
2012
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| Subjects: | |
| Online Access: | http://eprints.utem.edu.my/id/eprint/8877/1/PAPER_5_Neural_Network_Predictive_Control_%28NNPC%29_of_a_Deep_S.pdf http://eprints.utem.edu.my/id/eprint/8877/ |
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| Summary: | In this paper, the modeling and design of the depth control systems using Neural Network Predictive Control (NNPC)for a small unmanned underwater vehicle (UUV) will be described. Underwater vehicles consist of robotic vehicles that have been developed to reduce the risks of human life and to carry out tasks that would be impractical with a manned mission. The design of a depth control of an UUV is described in this paper. The main purpose of the underwater vehicle is that the vehicle must be stable over the entire range of operation. These techniques have the purpose of ensuring zero steady state error and minimum
error in response to step commands in the desired depth.The depth performance for NNPC is discussed in terms of
error and execution time. This NNPC will be compared with conventional controller such as PD controller and also by using the Fuzzy Logic Controller (FLC). For the comparison of computational time between this controllers, it can be observed that Fuzzy Logic is faster and neural network predictive controller is the slowest between them. It has been shown that the neural network predictive controller improved the transient response and error measure which shows the effectiveness of the designed controller.
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