Dual-Adaptive Super-Twisting Sliding Mode Path Tracking Control with Composite Observer Architecture Under Model Parameter Perturbations

The widespread adoption of unmanned agricultural machinery has transformed modern agricultural production. In unstructured farmland scenarios, variations in soil conditions and operational loads induce large perturbations to system dynamic parameters, leading to degraded tracking accuracy and insufficient robustness in fixed-parameter path tracking controllers. Additionally, conventional single-structure observers cannot simultaneously achieve fast convergence and smooth steady-state output. This study constructs a mixed preview error state-space equation, which aggregates parameter perturbations, unmodeled dynamics, and external disturbances into a unified lumped disturbance term of the system. A composite observer architecture is designed by parallelly combining an adaptive generalized super-twisting observer and a nonlinear extended state observer, where observation weights are continuously and smoothly scheduled via online identification of field operation conditions. Furthermore, a gain-power dual-adaptive super-twisting sliding mode control strategy is proposed, and the closed-loop stability of the system is rigorously proven. Co-simulation and field experiments covering powered rotary tillage, high-speed unloaded transfer, and variable tire pressure conditions verify that, under parameter perturbation, the increase in tracking error remains within 10%. Compared with the conventional PID controller used as a benchmark, the proposed controller reduces the root mean square error (RMSE) of lateral deviation by 40–50%, and maintains centimeter-level tracking accuracy in field operations. The proposed method provides technical support for high-precision operation of agricultural machinery in unstructured farmland environments.