基于模糊神经网络PID的无人艇航向控制器研究  

Research on unmanned surface vehicle heading controllerbased on fuzzy neural network PID

作　　者：王伟[1] 王勇[1] 周晨光 张晔[1] 寿康力 朱国栋 WANG Wei;WANG Yong;ZHOU Chenguang;ZHANG Ye;SHOU Kangli;ZHU Guodong(School of Mechanical Engineering,Hefei University of Technology,Hefei 230009,China)

机构地区：[1]合肥工业大学机械工程学院,安徽合肥230009

出　　处：《合肥工业大学学报（自然科学版）》2023年第4期458-462,共5页Journal of Hefei University of Technology：Natural Science

基　　金：安徽省科技重大专项资助项目(JZ2021AKKZ0042);安徽高校协同创新资助项目(GXXT-2019-031)。

摘　　要：针对常规比例、积分和微分(proportional integral derivative,PID)控制器在无人艇航向控制系统中表现出的稳定性差、控制精度低等问题,文章提出一种将模糊控制与反向传播(back propagation,BP)神经网络相结合的控制算法;在MATLAB中对比常规PID控制器、模糊PID控制器与模糊神经网络PID控制器在给定期望航向角下的航向控制性能,仿真结果表明模糊神经网络PID控制器对无人艇的航向控制性能最佳;在搭建的实验平台上对不同航向控制器下无人艇的航行轨迹和航向角进行比较,实验结果进一步验证了模糊神经网络PID航向控制算法的优越性。In order to solve the problems of poor stability and low control precision of conventional proportional integral derivative(PID)controller in unmanned surface vehicle heading control system,a control algorithm combining fuzzy control and back propagation(BP)neural network was proposed.The heading control performance of conventional PID controller,fuzzy PID controller and fuzzy neural network PID controller was compared under the given expected heading angle based on MATLAB.The simulation results show that the fuzzy neural network PID controller has the best heading control performance in unmanned surface vehicle.The above simulation results were further verified by comparing the trajectory and heading angle of unmanned surface vehicle under different heading controllers on the experimental platform.

关 键 词：无人艇 航向控制 模糊控制 反向传播(BP)神经网络 比例、积分和微分(PID) 

分 类 号：TP273[自动化与计算机技术—检测技术与自动化装置]