RESEARCH ON THE CONTROL SYSTEM OF MOBILE STRAW COMPACTION MOLDING MACHINE BASED ON PSO-ELM-GPC MODEL

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.