基于神经网络的压铸速度控制研究

Zhengsi Wu, Yifeng Wu, E. Zhang
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引用次数: 2

摘要

为了提高半固态压铸机注射系统的速度控制和精度,提出了一种基于BP神经网络的PID控制器来优化控制性能。本文分析了半固态压铸机注射系统的特点和控制原理,比较了现有控制算法的优点和局限性,在传统PID控制算法的基础上,采用神经网络算法对其参数进行在线调整,提高了整个系统的响应时间。本文建立了基于BP神经网络的PID控制器模型,并通过MATLAB和SIMULINK进行了在线仿真。结果表明,该控制器模型在系统响应时间、超调量和稳态误差方面均优于传统PID控制模型,满足半固态压铸机注射系统的控制要求。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Study on Die Casting Speed Control based on Neural Network
In order to improve the speed control and accuracy of the injection system of semi-solid die casting machines, this paper proposed a PID controller based on BP neural network to optimize the control performance. This paper analyzed the characteristics and control principles of the injection system of semi-solid die-casting machines, compared the advantages and limitations of the existing control algorithms, and based on the traditional PID control algorithm, adopted a neural network algorithm for online adjustment of its parameters to improve the overall system response time. This paper built a PID controller model based on BP neural network, and made online simulation through MATLAB and SIMULINK. Results showed that the controller model is better than the traditional PID control model in terms of the system response time, overshoot and steady-state error, and meets the control requirements of the injection system of semi-solid die casting machines.
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