Time-varying disturbances arising from complex terrain and the lack of rigorous constraint-handling mechanisms significantly degrade the path-tracking performance of unmanned agricultural tractors (UATs). To address these issues, this paper proposes a generalized extended-state-observer-based prescribed-performance sliding-mode (GESO-PPSM) control method. First, a homeomorphic mapping-based prescribed performance function is employed to impose hard performance constraints, guaranteeing that the preview error remains within predefined bounds throughout the entire process. Second, a generalized super-twisting extended-state observer (GESO) is developed to compensate for lumped uncertainties, enabling finite-time and high-accuracy disturbance estimation compared with that of conventional observers. Furthermore, a switching sliding mode surface is designed to achieve fast convergence far from equilibrium while effectively suppressing overshoot near the origin. Unlike traditional sliding mode control, a continuous path-tracking control law based on a power function is formulated to ensure robustness while avoiding discontinuities. Comparative co-simulations based on a high-fidelity UAT model demonstrate that the proposed control method achieves superior steady-state accuracy, with significant reductions in preview error standard deviations of up to 92.52%, 84.33%, and 80.44% compared to PID, model predictive control (MPC), and GESO-based conventional sliding mode (GESO-SM) control, respectively. These results validate the superiority of the GESO-PPSM method in terms of accuracy, robustness, and strict constraint satisfaction in complex agricultural environments.
Li et al. (Sun,) studied this question.