# The Current Development Status of Agricultural Machinery
## 4. Chassis Leveling Systems of Agricultural Machinery
### Control Systems of Leveling
[136] developed a mountain crawler tractor that applied separate PID and fuzzy PID control algorithms for chassis and implement leveling. The Simulink-based simulation model demonstrated rapid response and stable coordinated control, with its architecture illustrated in Figure 18. [PERSON] et al. [137] introduced the QBP-PID hybrid algorithm, which integrates Q-learning reinforcement mechanisms, BP neural networks, and PID control architecture to achieve online neural network weight optimization and real-time parameter tuning. [PERSON] et al. [138] proposed a single-neuron adaptive PID control algorithm that reduced response time by 32% compared to conventional PID, effectively resolving height instability during abrupt load changes in agricultural vehicles. [PERSON] et al. [139] presenteda BP neural-network-based automatic leveling method (Figure 19), demonstrating superior performance in stabilization time, maximum tilt overshoot, and steady-state error compared to PID control. [PERSON] et al. [140] proposed an adaptive dual-loop leveling system incorporating active suspension, employing composite dual-loop control and optimized adaptive genetic algorithms to enhance chassis leveling performance across diverse road conditions. [PERSON] et al. [141] developed a sliding mode variable structure control algorithm with fuzzy switching gain regulation for four-point leveling mechanisms, achieving real-time dynamic leveling with improved precision and stability. \begin{table}
\begin{tabular}{p{113.8 pt} p{113.8 pt} p{113.8 pt} p{113.8 pt}} \hline \hline
**Control Strategy** & **Core Advantages** & **Performance Defects** & **Performance Indicators** \\ \hline \multirow{4}{*}{Classic PID Control} & & & Max slope: 20–25°; \\  & Simple implementation; flexible adjustment & Poor nonlinear adaptability; weak disturbance rejection & 3.0–4.2 kJ/kg-km; response time: 300–500 ms; overshoot: 15–25\% \\  & & & Maximum slope: 22–27°; energy consumption per unit \\ Incremental & Strong anti-interference ability; easy to switch between automatic and manual modes & Poor adaptability to time-varying systems; high computational burden & mass: 2.8–3.8 kJ/kg-km; response time: 250–400 ms; overshoot: 10–20\% \\  & Strong adaptability; good robustness; high control accuracy & Complex rule-based design; limited real-time performance & mass: 2.3–3.0 kJ/kg-km; response time: 150–250 ms; overshoot: 5–12\% \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Comparison of agricultural machinery chassis leveling control strategies. Figure 16: Foreign intelligent leveling machinery. (**a**) CLAAS LEXION 8900 Harvester; (**b**) John Deere S790 Harvester; (**c**) Case IH Axial-Flow 250 Harvester; (**d**) Fendt IDEAL 10T Harvester. In summary, significant progress has been achieved in the research on agricultural machinery leveling control systems both domestically and internationally; however, distinct developmental patterns are evident between regions. Overseas technologies, particularly those in Europe, the United States, and Japan, have progressed to intelligent and high-precision stages. These systems demonstrate improved dynamic response, terrain adaptability, and energy efficiency through multi-sensor fusion, satellite positioning corrections, and advanced control algorithms. In contrast, domestic research primarily relies on PID-based frameworks, focusing on algorithmic enhancements to improve disturbance rejection and response speed. Although advancements have been made in overshoot suppression and parameter self-adaptation, the application of advanced strategies such as model predictive control (MPC) and sliding mode control remains at an exploratory stage, with limited integration of hardware systems like satellite positioning and active suspension. Globally, scholars have conducted pioneering research on intelligent algorithm integration and dynamic stability monitoring for unstructured terrains. However, China still faces notable technological gaps in engineering implementation and coordinated electro-hydraulic control.