Research on predictive control of a novel electric cleaning system for combine harvester based on data-driven

In order to optimize the efficiency of the combine harvester cleaning system, this research introduces a data-driven control approach merging subspace model identification and event-triggered adaptive model predictive control (ETAMPC) under a linear parameter-varying (LPV) system structure. This method addresses the challenges of modeling and controlling the cleaning system, treating it as a “black box”. It is further applied to a newly designed electric cleaning system (ECS), solving the problem of difficult real-time adjustment of fan speed and vibrating sieve frequency caused by mechanical coupling in conventional cleaning systems, and achieved coordinated control over fan speed and vibrating sieve frequency to reduce loss rate and impurity rate. The simulation results show that the accuracy of the predicted output of the constructed ECS identification model exceeds 85%, and the designed ETAMPC strategy not only exhibits good effect of performance tracking (with tracking errors remaining below 10% under random disturbances) but also effectively reduce computational load by approximately 50%. Field tests indicate that the designed ECS can reduce cleaning losses by 16% to 19% and impurities by 13% to 27%. This system offers a new pathway to enhance the operating performance of combine harvesters.