摘要
伺服控制直接决定了光电跟踪系统的性能,文章采用模糊神经网络控制算法,具有参数学习和结构学习功能,通过Matlab仿真对比发现无论是动态、静态性能还是鲁棒性方面都要优于传统的PID控制以及模糊控制,表现出很好的准确性和快速性,为光电跟踪系统伺服控制设计提供了一种可行的技术方案。
Servo control, which plays decisive role in electro-optical tracking system, directly determines the system performance, this essay focus on design a fuzzy neural network algorithm with parameters self-learning and structures self- learning, which have been proved that it has the advantages over traditional PID and fuzzy logic in dynamic and static per- formances as well as in system robustness by using the insertion SIMULINK in MATLAB, this algorithm improved perform- ances in accuracy and rapidity, and also provide a feasible technical solution for servo control.
出处
《舰船科学技术》
北大核心
2017年第4期122-126,共5页
Ship Science and Technology
关键词
光电跟踪
伺服控制
模糊神经网络
参数学习
结构学习
electro-optical tracking
servo control
fuzzy neural network
parameters self-learning
structures self-learning
