Trajectory Tracking Control of an Orchard Mobile Robot Based on an Adaptive Super-Twisting Extended State Observer and Dynamic Power–Logarithmic Sliding Mode Control

To address the degradation in drive-wheel speed control accuracy and vehicle trajectory-tracking performance caused by variations in wheel–terrain adhesion, load fluctuations, and model uncertainties under unstructured terrain conditions, a composite control method based on an adaptive super-twisting extended state observer and dynamic power-logarithmic sliding mode control is proposed. First, a kinematic model of a four-wheel differential-drive orchard mobile robot and a dynamic model of the drive motor incorporating lumped disturbances are established, and a dual closed-loop control system comprising a kinematic outer loop and a wheel-speed inner loop is constructed. Second, a super-twisting extended state observer with an adaptive gain adjustment mechanism is designed to estimate the drive-wheel angular acceleration and lumped disturbances online. Subsequently, a dynamic power-logarithmic sliding mode controller is developed, and the estimated disturbances are employed for feedforward compensation to improve wheel-speed response, disturbance-rejection performance, and control-input smoothness. Drive-system simulations, circular-trajectory simulations, and real-vehicle S-shaped trajectory-tracking experiments on natural turf are conducted to compare the proposed method with ESO–DCLSMC and DP–LnSMC. The results show that, in the drive-system simulations, the proposed method achieves a wheel-speed settling time of 50 ms and a maximum angular-velocity deviation of 0.009 rad·s−1 under pulse load disturbances. In the real-vehicle experiments, the root-mean-square errors of position and heading angle are 10.51 cm and 0.050 rad, respectively, representing reductions of 29.78% and 24.70% compared with ESO–DCLSMC and 36.88% and 42.17% compared with DP–LnSMC, respectively. These results demonstrate that, under the natural grass terrain tested in this study, the proposed method achieves favorable trajectory-tracking accuracy and provides a feasible solution for trajectory-tracking control of orchard mobile robots operating under unstructured terrain conditions.