基于BP神经网络的船舶航向智能PID控制研究

Research on intelligent PID control of ship course based on BP neural network

针对船舶航向控制非线性的特性,以船舶航向运动一阶KT模型为研究对象,设计了基于BP神经网络的自整定PID算法航向控制器。将传统PID与BP神经网络结合,对被控对象由BP神经网络进行辨识,给出PID控制参数,由PID控制算法进行控制并优化收敛速度。根据真实渡轮船舶特征参数,利用MATLAB/Simulink仿真软件建立船舶航向运动控制系统模型。仿真结果表明,基于BP神经网络的PID控制系统超调小、鲁棒性好,可长时间稳定工作,几乎无稳态误差,控制算法的实用性以及动态控制系统的优越性得到验证。

According to the nonlinear characteristics of ship course control,and taking the first-order KT model of ship course motion as the research object,the self-tuning PID algorithm course controller based on BP neural network was designed. Combining the traditional PID and the BP neural network,the controlled object was identified by the BP neural network,the PID control parameters were given,and the PID control algorithm was used to control and optimize the convergence speed. Based on a true ferry＇s characteristic parameters,the ship＇s course motion control system model was established using MATLAB/Simulink simulation software. The simulation results show that the design has small overshoot,good robustness,capable to work steadily for a long time,and almost no steady-state error. The practicability of the control algorithm and the superiority of the PID control system were verified.