为了提高常规PID控制器在非线性、时变不确定性系统中的控制性能，提出一种模糊神经网络PID控制算法，利用模糊控制良好的非线性控制优势，以及神经网络超强的自学习、自适应特性，实现对PID参数的实时在线整定，并建立船舶柴油机转速模糊神经网络PID控制系统数学模型，利用Matlab/Simulink进行仿真。仿真结果表明，模糊神经网络PID控制超调量少、精度高、调节时间短，具有更好的动静态特性和抗干扰特性，系统鲁棒性有了很大提升，能很好地满足船舶柴油机转速控制系统的要求。
In order to improve the control performance of nonlinear and time-varying uncertain system,a fuzzy neural network PID control algorithm is proposed,which realizes the real-time online tuning of PID parameters by taking advantage of the nonlinear control advantages of fuzzy control as well as the super self-learning and self-adaptive characteristics of neural network.The mathematical model of fuzzy neural network PID control for marine diesel engine speed is established, the simulation results show that the fuzzy neural network PID control has less overshoot, high precision, short adjustment time, better dynamic and static characteristics and anti-interference characteristics, and the robustness of the system has been greatly improved. It can well meet the requirements of speed control system of Marine diesel engine.
2022,44(21): 101-105 收稿日期：2021-10-14
DOI：10.3404/j.issn.1672-7649.2022.21.021
分类号：U664.121;TK427
基金项目：浙江省高校访问工程师“校企合作项目”（FG2017013）；浙江省高等教育“十三五”教学改革研究项目（jg20190699）
作者简介：徐红明(1978-),男,副教授,研究方向为船舶自动控制
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