This paper investigates the transmission performance of a novel dual-drive hydrostatic lead screw micro-nano feed system (DDHLS) that can obtain extremely low speed. Firstly, the oil film liquid friction of hydrostatic transmission is modelled, and the calculation model of oil film dynamic friction is proposed based on the variable viscosity theory. Secondly, on this basis, combined with the LuGre friction model, a novel all-components refinement friction identification method (ACRFIM) for DDHLS was developed. The friction parameters of the feed drive components such as LM guide and hydrostatic lead screw can be identified independently using the proposed method, ensuring precise friction force modelling in all components. Then, an all-component adaptive friction compensation control algorithm (AACA) was designed by introducing the temperature and disturbance influence factors into the friction model and considering the influence of the dynamic friction of liquid. The experiments illustrate that the calculation accuracy of the oil film friction model based on the variable viscosity theory is substantially improved. DDHLS can effectively suppress the adverse effects of nonlinear friction, and the proposed AACA has an obvious compensation effect for the friction of the time-varying system.

The dual drive sliding feed system can obtain a uniform and stable resolution at extremely low speeds and significantly reduce the system’s nonlinear friction. However, the numerous thermal sources within the system and the multipoint sliding contact during transmission result in a significant temperature rise, leading to considerable thermal deformation and errors. Moreover, the responsive mechanism of the thermal characteristics needs to be clarified. Therefore, firstly, a frictional torque model of the engagement of the screw and nut is established, and the heat generation, heat transfer, and thermal contact resistance (TCR) are solved. Then, based on the solution, a finite element thermal simulation model of the dual drive sliding feed system is established, and experiments are performed for validation. The results show that the error in temperature at the measuring point is less than 2.1 °C, and the axial thermal elongation of the screw is less than 6.2 µm. Finally, the thermal characteristics of the feeding system under various operating conditions are analyzed. The results show that the established thermal simulated model can effectively describe the dynamic thermal characteristics of the dual drive sliding feed system during operation. The effects of the rotational speed and ambient temperature on the dynamic thermal characteristics of the dual drive sliding feed system are investigated separately. The temperature increase in each part of the screw during the operation is characterized.

Autonomous mobile robots are playing an increasingly significant role in modern smart orchards by supporting precision agricultural operations such as target-oriented spraying and autonomous harvesting. Nevertheless, achieving high-precision trajectory tracking and stable motion in complex, unstructured orchard environments remains challenging, because tracking deviations induced by uneven terrain and low-traction soil can directly affect operational safety and efficiency. To address this challenge, the present study proposes an adaptive tracking controller which integrates model-driven and data-driven approaches. Firstly, a six-state planar dynamic model based on Newton–Euler equations is established to describe motion characteristics. Secondly, an improved Particle Swarm Optimization (PSO) algorithm is employed for offline parameter optimization under representative operating conditions. The process thus engenders a mapping dataset that relates the real-time motion states of the orchard mobile robot to the optimized horizon parameters and weights. Finally, an Adaptive Neuro-Fuzzy Inference System (ANFIS) is trained using this dataset, enabling adaptive adjustment of MPC parameters according to the robot motion state. Simulation and experimental results demonstrate that, in Double-Lane-Change (DLC) and serpentine simulations, the proposed controller reduced lateral and heading Root-Mean-Square (RMS) errors to 0.0109 m/0.0081 rad and 0.0102 m/0.0117 rad, achieving reductions of 49.30–85.58% and 68.60–88.02% compared with Pure Pursuit, Stanley, Linear Quadratic Regulator (LQR), and traditional MPC, respectively. In orchard field tests with circular and Figure-8 trajectories at 0.3–0.6 m/s, the lateral RMS errors were recorded as 0.0112–0.0182 m and 0.0156–0.0262 m, respectively, corresponding to reductions of 46.94–61.52% relative to traditional MPC, while the heading RMS error remained below 0.0510 rad. These findings substantiate the efficacy of the proposed controller in enhancing the accuracy and adaptability of the system, thereby providing a resilient and precise control framework for operation within orchard environments.

Periodic and non-periodic disturbances significantly affect the tracking accuracy of servo systems. A dual-motor drive composite control strategy based on iterative learning feedforward control and super-twisting sliding mode observer is proposed. Initially, a novel reaching law capable of dynamically adjusting gain coefficients based on system states is introduced, leading to the design of a sliding mode controller with proven asymptotic stability. To address non-periodic total disturbances, a super-twisting sliding mode observer is developed, and Lyapunov stability theory is employed to demonstrate system stability and error convergence to zero. A resonant controller is designed to suppress medium- to high-frequency periodic disturbances. For periodic total disturbances, a parameterized feedforward controller based on iterative learning is devised, and an input-shaping filter is introduced to refine the input trajectory. The feedforward control parameters are iteratively updated using a data-driven approach. Experiments are conducted on a differential dual-drive servo system. The nut motor adopts the sliding mode controller with an observer. The screw motor employs the iterative learning feedforward controller. Results show effective suppression of the disturbances. Speed ripple is reduced, and tracking accuracy is significantly improved. The study demonstrates the feasibility and advantage of combining robust control with iterative learning in high-precision servo systems.