Abstract
This paper studies a novel intelligent motion control algorithm for Autonomous Underwater Vehicles (AUV) and develops a virtual reality system for a new interactive experimental platform. The paper designs a robust neuro-fuzzy controller to tackle system uncertainties and external disturbances. Fuzzy control can solve the uncertainty problem of control systems. The neural network model self-tunes the controller parameters to improve the anti-interference ability. The designed control algorithm is verified using a MATLAB implementation and a virtual reality system. The virtual reality system developed in this paper can be used to debug the control algorithm, simulate the marine environment, and establish an ocean current interference model. The paper uses the MATLAB engine to realize the data communication between the MATLAB and the AUV virtual reality system. This allows the output order of the controller in MATLAB to drive the AUV in a virtual simulation system to simulate the 3D space motion.
Highlights
Autonomous Underwater Vehicles.AUV is a tightly coupled system which has to cope with high nonlinearity, uncertain motion, and severe disturbances
We proposed a kind of neuro-fuzzy controller design for path tracking, which can be adapted to any AUV without establishing a dynamic model of the AUV
The algorithm we studied is not based on the AUV dynamic model, the AUV dynamic model will be used in the following simulation and comparison with the traditional control method
Summary
There has been a lot of research on the design of path-following dynamic controllers for underactuated AUVs. Literature [5] applied the ILOS guidance law and PID controller to realize the horizontal path tracking control of underactuated AUV under ocean current disturbance. MATLAB provides many control toolboxes, such as the fuzzy toolbox, the neural network toolbox, and so on, which can be used to design the AUV tracking motion control algorithm. We proposed a kind of neuro-fuzzy controller design for path tracking, which can be adapted to any AUV without establishing a dynamic model of the AUV. Tracking control effects of the algorithm proposed in the paper are preliminarily tested in the MATLAB simulation environment. The data visualization of process control can be realized
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