An Artificial Neural Networks (ANN) Approach for 3 Degrees of Freedom Motion Controlling

Abstract

Maritime simulation systems provide opportunities to acquire technical, procedural, and operational skills without the risks and expenses associated with on-the-job training. Maritime simulation systems are tools used to simulate real-world scenarios for training and research purposes, in which they are used to train seafarers in a safe and controlled environment. These systems are used to simulate different scenarios, such as navigation, maneuvering, and ship handling. The simulation systems allow users to learn and practice different scenarios without exposing themselves to real-life risks. However, at the moment, Vietnam's maritime simulators are dependent on other nations, which results in a lack of technological autonomy, a lengthy transfer of technology, high expenses, and a reduction in national security. Therefore, there is a lot of interest in developing a domestic maritime simulation system. With a rotation angle of α = [α1 α2 α3]T from the PLC controlling the DC/Servo system, the motion platform of the marine simulation system is built on the Stewart platform design principle. Due to the use of conventional control methods, this system suffers from a time delay of up to 1200ms, which prevents it from reacting to real-time control. In this paper, we investigate a novel technique for controlling the dynamic model with three degrees of freedom (3 DOF) of a cockpit cabin deck using artificial neural networks. The findings demonstrate that the reaction to real-time control, rotation error, and drive/servo system movement are all greatly improved.