Abstract
Under the operation mode of “mobile motorized platform + multiple types of implements” widely adopted in the field of high value-added crop cultivation in greenhouse and other modern facility agriculture, the driving wheels of the mobile platform are very prone to slipping and destabilization due to the complex and variable soil conditions, uneven load distribution, and frequent changes in traction resistance, which seriously affects the operation quality, precision and reliability of the equipment, ultimately leading to reduced crop yields and increased operational costs.In this paper, to address these challenges, a new type of fully power decoupled electric facility agricultural operation platform with enhanced traction performance and energy efficiency is designed, and a model-based predictive algorithm for anti-slip control based on the optimal reference slip rate is proposed, which can effectively inhibit the problems of tire slippage and dragging in plowing operation by dynamically adjusting the driving torque in real time according to the actual soil conditions and operational requirements.In the study, the research process is systematically carried out in several key stages: firstly, a comprehensive dynamic model of the traction operation system of the operation platform is established by considering factors such as wheel-soil interaction mechanics, implement dynamics, and load transfer, to accurately determine the optimal reference slip rate of the operation platform under different soil conditions, including variations in moisture content, compaction, and texture; secondly, to achieve precise slip control, a model-based predictive algorithm anti-slip control with the optimal reference slip rate is proposed with the optimal reference slip rate as the control objective, incorporating real time slip rate estimation, predictive optimization, and feedback correction to ensure robustness under varying operational conditions.Finally, to validate the proposed control strategy, extensive experimental evaluations are conducted by changing the soil compactness and plowing depth in comparison with traditional control methods, and the effectiveness and robustness of the proposed control strategy are verified through quantitative performance metrics such as slip rate reduction, traction efficiency improvement, and operational stability enhancement. The experiments showed that, compared with no anti-slip control and sliding mode anti-slip control, the proposed control strategy demonstrated superior performance under different operational scenarios: it reduced the peak slip rate by 18.75%～71.91% and 7.14%～64.20% under the conditions of 1215 kPa soil tightness (tillage depth of 10 cm and 15 cm) and 525 kPa soil tightness (tillage depth of 15 cm), respectively, with standard deviation reductions of 29.58%～76.89% and 1.74%～63.75%, indicating significantly improved stability; additionally, the root mean square error is reduced by 17.06%～65.56%, demonstrating higher control accuracy; furthermore, the traction efficiency is improved by 4.47%～35.56% and 9.25%～17.55%, and the standard deviation is reduced by 39.30%～63.77% and 7.34%～50.82%, respectively, confirming enhanced energy utilization and operational consistency.The anti-slip control strategy proposed in this paper has significant advantages in reducing tire slip, enhancing operational traction performance, improving operational stability and straightness of operational trajectory, which not only provides a new method for anti-slip control of fully power decoupled electric facility agricultural operation platform, but also improves the operational quality under complex traction conditions, thereby contributing to the advancement of precision agriculture and sustainable farming practices. The findings of this study offer valuable insights for the design and optimization of intelligent agricultural machinery, paving the way for future research on adaptive control systems in agricultural automation.
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