Fuzzy backstepping controller for agricultural tractor-trailer vehicles path tracking control with experimental validation - Wang, Anzhe; Wang, Yefei; Ji, Xin; Wang, Kun; Qian, Meiling; Wei, Xinhua; Song, Qi; Chen, Wenming; Zhang, Shaocen (Frontiers in Plant Science, 2024)
Unmanned driving technology for agricultural vehicles is a crucial component in advancing modern agriculture toward precision, intelligence, and sustainability. As a widely utilized machinery in agricultural production, the autonomous driving technology of agricultural tractor-trailer vehicles (ATTVs)—comprising large implements connected to tractors via hitch points—has garnered significant attention in recent years. The primary objective of current research on autonomous driving technology for tractor-trailers is to enable the tractor to follow a reference path while complying with the constraints imposed by the trailer, which often do not align with agronomic requirements. To address this issue, this paper prioritizes the path tracking performance of implements (i.e. trailers), with the aim of enhancing the quality and efficiency of ATTVs during field operations. It proposes a fuzzy back-stepping path tracking controller based on the kinematic model of ATTVs, effectively addressing the aforementioned practical challenges. Initially, the path tracking kinematic error model with the trailer as the positioning center was established in the Frenet coordinate system using the velocity decomposition method. Subsequently, the path tracking controller was designed employing the back-stepping algorithm, which calculated the target front wheel steering angle of the tractor, while the gain coefficient was adaptively adjusted through a fuzzy algorithm. Finally, co-simulation and experiments were conducted using MATLAB/Simulink/CarSim and a physical platform, respectively. The simulation results indicated that the proposed controller reduces the trailer's online time by 36.33%. Moreover, the trailer's tracking error while following a curved path was significantly lower than that of the Stanley controller designed for a single tractor. In the actual experiment, while tracking the U-turn path, the proposed controller reduced the average absolute value of the trailer's path tracking lateral error by 65.27% and the maximum lateral error by 87.54%. The mean absolute error (MAE) values for lateral error and heading error were 0.010 and 0.016, respectively, while the integral of absolute error (IAE) values were 1.989 and 2.916, respectively.