## 2 Literature Review
\begin{table}
\begin{tabular}{c c c} \hline \hline \multicolumn{3}{c}{**Differences in Application Performance of Different Methods**} \\ \hline
**Literature** & **Advantage** & **Shortcoming** \\ \hline [PERSON] et al. [8] & The planning process of a straight & There are limitations to the path in \\ [PERSON] et al. [8] & reference path is intuitive and can quickly & complex scenarios; not considering the \\  & adjust the target path in the initial state & impact of terrain or environmental \\ \hline [PERSON] et al. [9] & The autonomy of exercise is guaranteed; a & \\ [PERSON] et al. [9] & mobile system with strong adaptability & May rely on accurate algorithms and data \\  & can effectively respond to environmental & support; the training requirements for the \\  & changes & algorithm are too high \\ \hline [PERSON] et al. [10] & Economy; can effectively integrate deep & May be limited by the performance of low \\  & visual information and action planning & distance hardware; real-time performance \\ \hline [PERSON] et al. [11] & Reinforcement learning and convolutional & Network training is difficult and requires \\  & neural networks for automation and & a lot of data and resources \\  & predictive analysis & \\ \hline [PERSON] et al. [12] & Capable of completely autonomous & The complexity of system integration and \\  & navigation & its dependence on multiple sensors \\ \hline [PERSON] et al. [13] & Strong adaptability in complex & Processing delays may affect real-time \\  & environments; can define areas of interest & response; poor winning ability \\ \hline [PERSON] et al. [14] & Enhanced control accuracy and stability & May be overly dependent on specific \\  & parameters; difficulty in debugging & \\ \hline [PERSON] et al. [15] & Can provide theoretical support for & Dependence on the suspension system \\  & dynamic control in complex environments & may result in additional costs. \\ \hline [PERSON] et al. [16] & Good control performance & Variables are difficult to accurately \\  & & calibrate \\ \hline [PERSON] et al. [17] & Capable of adapting to complex and & Timely control feedback requires high \\  & ever-changing agricultural environments & system requirements \\ \hline [PERSON] [18] & Stable control effect & Not universally applicable; the \\  & construction process is quite complex \\ \hline [PERSON] et al. [19] & Automatic control enhances work & The experimental results may be limited \\  & efficiency & by specific conditions \\ \hline [PERSON] et al. [20] & Strengthen dynamic control stability; & Requires high computing resources; \\  & enhanced the adaptability and flexibility of the system & dependent on multiple environmental \\ \hline [PERSON] et al. [21] & Improved the stability of the path tracking & Poor universality \\  & system & \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Differences in application performance of different methods. In summary, for unmanned agricultural machinery algorithms, most scholars use positioning algorithms and modelling control algorithms for research, which may perform well in specific environments or tasks, but less consideration is given to the stability of control algorithms applied to agricultural equipment. Therefore, this study proposes an improved parameter-adjustable, multi-deviation feedback fuzzy control method and presents a complete design scheme based on the agricultural machinery drive and operation control system to ensure its performance accuracy and application effect. The method proposed by this research institute has a simple design, is more adaptive and flexible, and can better meet the requirements of modern agricultural automation operations.