Abstract

In view of the inherent non-linearity, complexity, susceptibility to external wind, wave, and current interference of under-driven ships, and the difficulty of adjusting and adjusting control parameters, to improve the performance of ship’s autopilot, a kind of RBF neural network sliding mode variable structure PID controller is designed. Traditional PID control is sensitive to parameter changes, online tuning is difficult, and easy to overshoot. In order to solve this problem, combining the variable structure characteristics of PID, a differential compensation term is added to the integral term to convert the PID control parameters into three parameters with more obvious physical meanings, and then combined with the RBF neural network learning and identification function to realize online tuning and adaptive control of ship control parameters. Using MATLAB software to simulate the container ship “MV KOTA SEGAR” MMG model shows that the designed RBF neural network sliding mode PID controller can effectively eliminate the ship’s lateral deviation caused by external interference such as wind, waves, currents, etc., with high control accuracy,robustness and strong adaptability.

Highlights

  • Since substantive progress was made in the development of gyro compasses in the early 1920s, ship heading control has always been an important research topic in the field of ship motion control [1]

  • The basic problem of PID control is the contradiction between integral overshoot and steady-state error, and the ship motion state has the characteristics of large time delay, large inertia, and nonlinearity, which makes it more difficult to select the parameters of the ship's PID controller

  • The PID controller is the core of the entire control system, and its PID parameters play a vital role in its control quality

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Summary

Introduction

Since substantive progress was made in the development of gyro compasses in the early 1920s, ship heading control has always been an important research topic in the field of ship motion control [1]. Conventional PID autopilots can only adopt the "dead zone" method in the face of real-time changing sea conditions. This method reduces the accuracy of ship control while increasing energy consumption. Traditional methods are usually empirical, and it is difficult to meet the requirements in the face of complex control situations, while intelligent algorithms can better meet the parameter optimization of PID complex control situations. Aiming at the problem of ship navigation in time-varying sea conditions, the RBF neural network learning algorithm is introduced into the design of the sliding mode PID controller, which eliminates the need to estimate external disturbances such as wind, waves, and currents to achieve adaptive control of under-driven ships.

Ship steering motion equation
Controller design
RBF neural network sliding mode PID control design
Simulation verification and analysis
Simulation analysis of ship heading control in interfering sea state
Findings
Conclusions
Full Text
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