# Study of Motion Control and a Virtual Reality System for Autonomous Underwater Vehicles
## 1 Introduction
To address the above problems, the intelligent control technologies such as fuzzy control, neural network have been gradually used in AUV tracking control, since the intelligent control technology requires no mathematical model and the system has good robustness. Fuzzy control is an effective approach to resolve the control problem of the uncertainty system. The neural network is very strong in self-learning. A kind of neuro-fuzzy controller for AUV tracking proposed in this paper is a fuzzy system based on a neural network, which applies the learning function of a neural network to a fuzzy system so that the fuzzy system can automatically adjust and obtain fuzzy rules and membership parameters from the learning. The neuro-fuzzy model can constantly modify the membership function of the fuzzy subset and update it automatically, so it is a fuzzy system with strong self-adaptability. In recent years, virtual reality (VR) technology has played a vital role in scientific research and production in many fields such as aerospace, travel, urban construction, traffic management, defense and military [8; 9; 10; 11; 12]. Industry 4.0 is heading into a fusion of real world and the virtual world. Virtual reality technology provides many advantages such as operational safety, ease of use, high interactivity, and hence provides a new interactive experimental platform for modern scientific research. The virtual reality system for AUV motion control developed in this paper is programmed using Visual C++ to design the user interface and the AUV motion display interface. MilkShape 3D and OpenGL are adopted to model the AUV and the marine environment. The MATLAB engine is used to realize the data communication between MATLAB and the virtual reality system. During simulation, the AUV model and submarine model are designed, the marine scene is constructed, and the parameters are adjusted. 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. The simulation system also implements several marine conditions, such as different ocean current interferences to run the AUV motion control simulations. Our research focuses on the following two aspects:  
1. 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. An improved learning algorithm proposed can reduce the amount of calculation in the process of finding the error function gradient and improve the learning efficiency of the network. The effectiveness of the algorithm and the accuracy of its theoretical analysis are verified by numerical experiments. 2. The proposed algorithm is simulated and experimented taking several aspects into consideration. Tracking control effects of the algorithm proposed in the paper are preliminarily tested in the MATLAB simulation environment. A visual simulation platform has been developed to test the proposed algorithm, which can not only observe the movement of the AUV but also observe the output value of control quantity in real-time. The data visualization of process control can thus be realized.