## 1 Introduction  
Mechanical corn harvesting is already commonplace in most of China's maize-growing regions [1]. To increase the quality of corn harvesting, it is essential to reduce losses during harvesting. The loss of ears may be significantly reduced, and harvesting quality can be much improved, when the harvester platform is placed in opposing rows. To accomplish row harvesting, corn harvesters in China now have a low level of intelligence and depend heavily on manual steering during harvesting, which results in a high level of labor intensity for the driver, as well as an inability to guarantee harvest quality. In order to cut labor expenses, reduce harvest losses, and increase quality, an autonomous row alignment system for corn harvesters is needed. Corn harvester cutting table has been extensively investigated by domestic and foreign researchers because of their critical role in harvesting the crop. Based on a thorough examination of corn-picking mechanism boundary conditions, [PERSON] et al. calculated the impact of picking loss [2]. The seed removal and cob removal models were established to determine the best combination of parameters for corn picking and harvesting. [PERSON] et al. designed a low-injury corn picking mechanism and verified through experimentsthat the primary and secondary order of influencing the damage rate of the cob was the picking roller speed, spring stiffness, and forward speed [3]. [PERSON], NS developed a corn grain loss assessment system comprised of a machine vision image system to evaluate cutter seed loss and harvest loss [4]. These studies have achieved good research on the factors influencing the loss of the cutting table and the determination of the loss rate, but most of these studies have only obtained the influence law of individual factors and the optimal combination parameters, and there is a lack of research on how the harvest quality of the cutting table can be improved and the specific measures to reduce the harvest loss of the cutting table. Since the advent of RTK satellite positioning systems, which have been widely used for AB line navigation in agricultural machinery [5; 6; 7; 8; 9], autonomous driving and automatic navigation have become increasingly popular in the agricultural industry. In corn harvesters, however, the cutting table mechanism must always be aligned with the AB line driving mechanism. Alignment detection and body steering control are the two most important aspects of the automatic alignment system. In terms of row perception, in 2000, [PERSON] developed a machine vision-based hoe guidance system capable of achieving 5.0 cm at a vehicle speed of 3.5 m/s with high system accuracy [10]. [PERSON] developed and conducted field trials of a machine vision navigation system for a corn combine harvester [11]. The system separated the uncut crop rows from the surrounding background material, parameterized the corn rows, and calculated their position signals. [PERSON] et al. designed a new type of corn cutting table based on image recognition that can automatically align rows, collecting corn images by CCD cameras, analyzing and processing them with LabVIEW software, segmenting corn stalks in the case of natural lighting and complex farmland background, discerning the true distance between corn plants, and finally completing the automatic adjustment of cutting table row spacing [12] ([PERSON], 2017). Automatic row alignment on corn harvesters is difficult because of the challenging working conditions of the dusty field during harvest and the numerous variables involved in the operation. Due to these challenges, visual image processing technology is unable to function well. How to accurately perceive the maize rows to be harvested is a pressing issue in automatic row alignment research that must be resolved immediately. In terms of body steering control for automatic alignment systems, agricultural machinery is often used on the electro-hydraulic control method, motor control method [13; 14].