Actuators

To tackle the challenges faced by traditional wheeled tractors, whose steering systems have low flexibility and a large turning radius, and thus make turning hard in small fields and greenhouses, this paper proposes a differential steering control technology for battery-electric unmanned tractors. This innovative approach enables zero-radius turning while delivering environmental and economic advantages. Firstly, the system architecture and key components of the battery-electric unmanned tractor with differential steering are designed, including the mechanical structure, wheel-drive system, electrical system, and power battery. Based on the proposed system architecture, a multi-physics coupled model is established, covering the motor, reducer, battery, driver, vehicle body, and the relationship between tires and road surfaces. A multi-closed-loop control algorithm, regulating both the motor speed and yaw angular velocity of the tractor, is developed for differential steering control. The validation, conducted via a digital simulation platform, yields critical state curves for motor current, torque, speed, and vehicle rotation. This study establishes a novel theoretical framework for unmanned tractor control, with prototype development guided by the proposed methodology. Experimental validation of zero-radius steering confirms the efficacy of differential steering in battery-electric platforms. The research outcomes provide theoretical basis and technical references for advancing intelligent and electric agricultural equipment.

High-precision position control and pressure control are core performance requirements for modern electro-hydraulic actuators. While the design of controllers for high-performance position servo systems is relatively straightforward, the development of pressure control strategies for electro-hydraulic actuators poses substantially greater challenges. This is primarily due to the fact that unknown time-varying parameters, load dynamics, and sensor-induced measurement noise within the system drastically deteriorate the performance of the closed-loop system. To address these challenges, this study proposes an adaptive output feedback pressure controller specifically tailored for electro-hydraulic servo systems. This controller not only exhibits insensitivity to dynamic load disturbances but also effectively mitigates the adverse effects of time-varying parameters and sensor measurement noise. Theoretical analysis demonstrates that the proposed controller can guarantee the asymptotic stability of the system’s tracking error. Furthermore, detailed simulation and experimental results are presented to validate the superiority of the designed controller over conventional control strategies.

Electro-hydraulic servo systems are characterized by significant nonlinearities. Reinforcement learning (RL), known for its model-free nature and adaptive learning capabilities, presents a promising approach for handling uncertainties inherent in such systems. This paper proposes a predefined-time tracking control scheme based on RL, which achieves fast and accurate tracking performance. The proposed design employs an actor–critic neural network strategy to actively compensate for system uncertainties. Within a conventional backstepping framework, a command-filtering technique is integrated to construct a predefined-time control structure. This not only circumvents the issue of differential explosion but also guarantees system convergence within a predefined time, which can be specified independently by the designer. Simulation results and comparisons validate the enhanced control performance of the proposed controller.