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.
Song et al. (Thu,) studied this question.