In order to solve the labor shortage problem, unmanned agricultural vehicles have been widely promoted in China. Among these vehicles, unmanned tractor-trailers have garnered the most interest thanks to their flexibility and efficiency. However, trapped by the factors of vehicle parameter uncertainties, unstructured farmland roads, etc., the current unmanned tractor-trailers have shown poor tracking accuracy and longer online times in straight-line path tracking. To overcome the aforementioned issues, a fuzzy sliding mode control (FSMC) approach is proposed in this paper. First, the tractor-trailer path tracking error model was established based on the kinematic model and a reference model. Then, according to the sliding mode control (SMC) theory, the FSMC was designed. Through a Lyapunov theory analysis, the proposed control method can ensure that the articulated angle, the position error, and the heading error all converge to zero. Finally, field tests and Simulink simulations demonstrated the effectiveness and robustness of the suggested control mechanism. According to field experiments, the proposed control method can increase the trailer steady-state tracking accuracy by between 10.5 and 36.8% and the tractor steady-state tracking accuracy by between 11.1 and 50%.

Global agriculture is confronting unprecedented pressures from population growth, diminishing arable land, and severe rural labor scarcity, necessitating the advancement of intelligent agricultural equipment. As a core component of precision farming, unmanned agricultural tractors demand highly accurate and robust path tracking control. However, conventional methods often fail to cope with unstructured terrain and dynamic wheel slip under real field conditions. This paper proposes an extended state observer (ESO)-based improved Stanley guidance law, which incorporates real-time sideslip angle observation, adaptive preview-based path curvature compensation, and a sliding mode heading controller. The ESO estimates lateral slip caused by varying soil conditions, while the modified Stanley law utilizes look-ahead path information to proactively adjust the desired heading angle during high-curvature turns. Both co-simulation in Matlab-Carsim and field experiments demonstrate that the proposed method significantly reduces lateral tracking error and overshoot, outperforming classical algorithms such as fuzzy Stanley and sliding mode controller, especially in U-turn scenarios and under low-adhesion conditions.

With the rapid development of unmanned agricultural machinery technology, the accuracy and stability of agricultural machinery path tracking have become key challenges in achieving precision agriculture. To address the issues of insufficient accuracy and stability in path tracking for rice transplanters in paddy fields, this study proposes a composite control strategy that integrates the extended line-of-sight (LOS) guidance law with an adaptive fuzzy sliding mode control law. By establishing a two degree of freedom dynamic model of the rice transplanter, two extended state observers are designed to estimate the longitudinal and lateral velocities of the rice transplanter in real time. A dynamic compensation mechanism for the sideslip angle is introduced, significantly enhancing the adaptability of the traditional look-ahead guidance law to soil slippage. Furthermore, by combining the approximation capability of fuzzy systems with the adaptive adjustment method of sliding mode control gains, a front wheel steering control law is designed to suppress complex environmental disturbances. The global stability of the closed-loop system is rigorously verified using the Lyapunov theory. Simulation results show that compared to the traditional Stanley algorithm, the proposed method reduces the maximum lateral error by 38.3%, shortens the online time by 23.9%, and decreases the steady-state error by 15.5% in straight-line path tracking. In curved path tracking, the lateral and heading steady-state errors are reduced by 19.2% and 14.6%, respectively. Field experiments validate the effectiveness of this method in paddy fields, with the absolute lateral error stably controlled within 0.1 m, an average error of 0.04 m, and a variance of 0.0027 m².

In order to improve the control accuracy and adaptability of drying control systems in complex paddy rice drying processes, the Gaussian process regression model predictive (GPR-MPC) drying process control strategy is designed. The strategy integrates the advantages of drying mathematical models and artificial intelligence algorithms. Firstly, based on the predicted moisture content of the drying mathematical model and the moisture content detection value, the Gaussian process regression is used to establish the model of moisture content prediction error. Secondly, the GPR-MPC control system is designed and simulation experiments are conducted to verify its effectiveness. Finally, the GPR-MPC intelligent control system of a grain dryer is designed and drying experiments are conducted with the grain dryer. The GPR-MPC intelligent control system testing experiment is conducted using a 15-ton cross-flow batch type recirculating grain dryer. The experimental result shows that the maximum, average, and variance of the grain moisture content control error are 0.4%, 0.165% and 0.114%, respectively. Compared to the MPC control system, the designed GPR-MPC intelligent control system has high prediction accuracy, small moisture content control error, and stable control system operation. The integration of drying mathematical models and artificial intelligence algorithms can effectively improve the drying effect and reduce dependence on data volume. This research is of great significance for promoting the development of intelligent drying technology.