PSO approach for a controller based-on backstepping method in stabilizing an underactuated X4-AUV
The autonomous underwater vehicle (AUV) mostly has fewer control inputs than the degree of freedoms (DOFs) in motion and be classified into underactuated system. It is a difficult task to stabilize that system because of the highly nonlinear dynamic and model uncertainties, therefore it is usually r...
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| Main Authors: | , |
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| Format: | Conference or Workshop Item |
| Language: | English |
| Published: |
2017
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| Subjects: | |
| Online Access: | http://umpir.ump.edu.my/id/eprint/21225/1/16.%20Pso%20approach%20for%20a%20controller%20based-on%20backstepping%20method%20in%20stabilizing%20an%20underactuated%20x4-auv.pdf http://umpir.ump.edu.my/id/eprint/21225/ |
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| Institution: | Universiti Malaysia Pahang |
| Language: | English |
| Summary: | The autonomous underwater vehicle (AUV) mostly has fewer control inputs than the degree of freedoms (DOFs) in motion and be classified into underactuated system. It is a difficult task to stabilize that system because of the highly nonlinear dynamic and model uncertainties, therefore it is usually required nonlinear control method to control this type of system. Conventionally, to control the system, parameters for the controller are selected by the trial-and-error method or manually chosen. It is challenging to get satisfactory responses because manual tuning is not an easy task and consuming much time, especially involve many parameters. It is necessary to select proper parameters because an improper selection of the parameters may jeopardize the system stability and leads to inappropriate responses. Thus, an optimization technique is required in selecting the optimal parameter for the controller. In this paper, the controller based on backstepping method is required for an underactuated X4-AUV system. Three types of controller based on backstepping are designed ; standard backstepping, PID backstepping and integral backstepping. Twelve optimal parameter values are generated for each controller using particle swarm optimization (PSO). All these three controllers show an improvement in term of settling time, and it has rapid responses compare than a controller with manual tuning parameters. The effectiveness of the controllers is verified in a computer simulation using MATLAB software. |
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