Traveling control method adapted to different paddy ground conditions with feedforward compensation for crawler combine harvester based on online tracking error prediction

Abstract

Enhancing the adaptability of agricultural machinery to different ground conditions is paramount for advancing path-tracking accuracy and control stability. Precise online perception of the steering motion state of a crawler combine harvester is essential for adapting to different paddy ground conditions (PGCs) for path-tracking control. In this study, a novel pose vector method (PVM) was proposed for the online determination of the steering radius of a crawler combine harvester. A support vector regression (SVR) model was used to forecast the error-correction factor for the steering trajectory arc radius, and a feedforward compensation control method was designed based on the steering control model. Field steering experiments revealed that the PVM outperformed the conventional least squares method (LSM) in calculating the trajectory arc radius, particularly when dealing with sparse pose data. The relative error of the PVM was several to over 10 times lower than that of the LSM fitting. The field experiments of straight-path tracking showed that the standard deviations of the lateral deviation were 0.078 m, 0.059 m, and 0.062 m, and the standard deviations of the heading deviation were 2.622°, 1.580°, and 1.593°, respectively, for the three different PGCs of soil compaction from low to high. Compared to the conventional pure tracking algorithm, the proposed PGCs adaptive tracking control algorithm enhances the adaptability of the crawler combine harvester to PGCs. These results offer a novel method for enhancing the online perception of the motion state of a crawler combine harvester and improving the adaptive control performance under different PGCs.