# The Current Development Status of Agricultural Machinery
## 4. Chassis Leveling Systems of Agricultural Machinery
### Control Systems of Leveling  
In the field of slope-adaptive leveling technology for agricultural machinery, the dynamic stability and response speed of leveling control systems are recognized as key performance evaluation metrics. Figure 15: “Three-Layer Articulated Frame” tracked work machine. Compared with domestic developments, international automatic leveling systems--particularly those in the United States, Western Europe, and Japan--have transitioned from purely mechanical adjustments to intelligent, high-precision systems through the integration of satellite positioning, sensor networks, and automated control algorithms. These technological advancements have significantly improved operational accuracy, efficiency, and terrain adaptability. CLAAS implemented a model predictive controller (MPC) in the LEXION 8900 harvester (Figure 16a), which generates real-time leveling commands by solving vehicle dynamics equations. This system is further enhanced by crawler-mounted spread spectrum satellite corrections, enabling it to operate effectively on slopes up to 30\({}^{\circ}\), with an 18% reduction in energy consumption compared to traditional PID controllers. The John Deere S790 combine harvester (Figure 16b) integrates dual-axis tilt sensors and ground radar elevation scanners to dynamically detect terrain variations. Using model predictive control (MPC), the system adjusts header inclination based on forward travel speed, achieving a leveling response time of less than 0.5 s. The Case IH Axial-Flow 250 series harvester (Figure 16c) features a maximum leveling range of \(\pm 6^{\circ}\), capable of correcting body tilt within 0.8 s with an error margin of less than 0.3\({}^{\circ}\). AGCO's Fendt IDEAL 10T combines electromagnetic active suspension with synchronized adjustment of threshing drum tilt and header height, reducing vibration amplitude by 70% and completing leveling operations within 1.2 s (Figure 16d). [PERSON] et al. [133] proposed a dynamic stability monitoring method based on an online adaptive observer, effectively addressing the limitations of conventional road dynamics approaches when applied to unstructured terrains. This approach provides a novel technical pathway for assessing rollover risks during off-road agricultural operations. [PERSON] et al. [134] developed HydraROPS, a dual-mode automatic rollover protection system that employs a hydraulically driven mechatronic architecture to regulate lateral posture and enhance safety during complex terrain operations. Current control strategies for agricultural machinery leveling in China primarily rely on PID-based frameworks, encompassing classical PID, incremental PID, and various enhanced variants. Representative approaches include fuzzy PID (dynamically adjusting parameters via fuzzy logic to address nonlinear disturbances), dual-loop fuzzy PID (improving disturbance rejection through position-velocity dual feedback), and BP neural network PID (optimizing control parameters through neural network self-learning). A limited number of studies have explored advanced methods such as sliding mode control and model predictive control (MPC). Table 4 presents a comparative analysis of the advantages and disadvantages of different PID control strategies. [PERSON] et al. [135] proposed an automatic leveling control method based on a dual-loop fuzzy PID algorithm (\(u(t)=k_{p}e(t)+k_{i}l_{0}^{\dagger}e(t)dt+k_{d}\frac{de(t)}{dt}\), \(k_{p}=k_{p0}+\Delta k_{p},k_{i}=k_{i0}+\Delta k_{i},k_{d}=k_{d0}+\Delta k_{d}\) In the formula, \(u(t)\) represents the output of the controller; \(e(t)\) represents the input of the controller; \(k_{p}\) is the proportional gain; \(k_{i}\) is the integral gain; \(k_{d}\) is the derivative gain; \(k_{p0},k_{i0}\), and \(k_{d0}\) are the initial values of the PID parameters) to adjust the vehicle posture (Figure 17). Under identical PID parameters, this approach outperformed conventional dual-loop PID control by effectively reducing overshoot and leveling time. [PERSON] et al.