基于改进PID神经元网络的刮板取料解耦控制  

Decoupling Control of Scraper Reclaim Based on Improved PID Neural Network

作　　者：郭凯旋 曲金星 岳益锋 王群英 

机构地区：[1]华电电力科学研究院有限公司,浙江杭州310000

出　　处：《工业控制计算机》2020年第8期73-75,共3页Industrial Control Computer

摘　　要：刮板堆取料机是煤场取煤过程中的重要设备,堆取料机的无人值守及自动控制成为当前研究的热点。刮板堆取料机自动取料过程中的料流稳定是自动控制的关键点之一,针对不同煤种的不同特性,自动控制参数应有所不同。刮板堆取料机取料过程实际是刮板运动、取料臂俯仰、回转机构协调并行工作的结果,是一个耦合的工作系统。为了精准地控制取料流量以及刮板的运动轨迹,需实现以取料刮板、取料臂俯仰机构、回转机构这三个机构的解耦控制。提出一种基于改进的PID神经元网络解耦控制方案来自动调整刮板取料过程参数,使系统能够在不同煤种的取料情况下更精确、快速地控制取料流量。The stability of the material flow during the automatic extraction process is one of the key points of the automatic control.According to the different characteristics of different coal,the control parameters should be different.The extraction process of the reclaimer is actually the result of coordinated and parallel work of the scraper movement,the pitch angle of the reclaimer arm and the slewing mechanism.It is a coupled working system.In order to accurately control the flow and the scraper trajectory,it is necessary to realize decoupling control of the three mechanisms:the scarper,the pitching mechanism and the slewing mechanism.This paper proposes an improved PID neural network decoupling control scheme to automatically adjust the parameters of reclaiming process,so that the system can more accurately and quickly control the flow in the case of different coal types.

关 键 词：刮板堆取料机 取料控制 恒流量 PID神经元网络 解耦控制 

分 类 号：TP2[自动化与计算机技术—检测技术与自动化装置]