Authors

Abstract

To address the issue of mutual influence and coupling between the main shaft speed and feeding amount of the mobile straw compaction molding machine, which is beneficial for the intelligent operation of the compaction molding, this paper designs a PSO-ELM-GPC control model. This model integrates Particle Swarm Optimization (PSO) algorithm, Extreme Learning Machine (ELM), and Generalized Predictive Control (GPC). It uses the ELM optimized by PSO to predict the output of the main shaft speed and feeding amount, and adjusts the input of the GPC controller based on the deviation weight adjustment unit. Field simulation experiments show that the maximum dynamic deviation of the speed is 1.72%, and the deviation from the target value is 1.52%. The maximum dynamic deviation of the feeding amount is 1.22%, and the deviation from the target value is 1.42%. The PSO-ELM-GPC model designed in this paper can promptly correct the uncertainties in speed and feeding amount control caused by disturbances.

Abstract in Chinese

为解决移动式秸秆致密成型机主轴转速与喂入量相互影响相互耦合的问题，以利于致密成型机智能化作业，本文设计了PSO-ELM-GPC控制模型，集粒子群优化、极限状态机、广义预测控制于一体，采用粒子群优化后的极限状态机对主轴转速与喂入量做出预测输出，依据偏差权重调整单元对GPC控制器输入量做出调节。场地模拟试验表明，转速最大动态偏差为1.72%，与目标值的偏差为1.52%；喂入量最大动态偏差为1.22%，与目标值的偏差为1.42%。本文设计的PSO-ELM-GPC模型可及时校正干扰引起的转速与喂入量控制引起的不确定问题。