Abstract: Weeds as co-occurring vegetation have posed significantly impacts on the growth and yield of wolfberry during cultivation. As such, effective weed management is critical to the wolfberry orchard maintenance. However, most currently available hydraulic drives can lead to the complex structures and substantial weight in the existing intra-row weeding machines. The maneuverability of the equipment can also be constrained to increase manufacturing complexity and cost, leading to the low operational efficiency. In this study, an intra-row electric electric-driven weeder was designed with for obstacle avoidance. The weeder consisted of a frame, the take-off and landing mechanism, transmission, weeding, obstacle avoidance, and power supply. The frame served as the overall installation carrier for obstacle avoidance and weed removal machines. The weeding device mainly consisted of a DC motor module, chain transmission, and weeding cutter. The DC motor module was provided the power for the rotation of the weeding cutter through a chain drive. The automatic obstacle avoidance device was composed of a signal acquisition mechanism, servo motor modules, and a four-bar mechanism. The weeder was powered by an external 24V DC generator and a battery pack. The automatic obstacle avoidance control system consisted of signal acquisition, monitoring feedback, program control, and a servo motor module. The signal acquisition was used to obtain the accurate position information of wolfberry in real-time, and then transmitted into the program control section. The theoretical driving signal was then calculated to dynamically control the speed and rotation angle of the servo motor module. The monitoring feedback section was used to determine the deviation that generated by the contact between the contact rod and wolfberry plants. The difference between the preset deviation threshold was monitored in real time. The feedback information was then transmitted to the program control section, which was compared with the theoretical driving signal of the servo motor module, and then precisely adjusted the actual obstacle avoidance. Furthermore, the hardware selection and software design of the closed-loop control system were carried out on the control system of automatic obstacle avoidance. The PID (proportion-integral-differential) parameters of the control program were adjusted at the operating speed of 400 mm/s. The optimal combination of PID parameters was achieved: K_P=56.00, K_i=0.006, and K_d=34.74. A virtual prototype model of weeder was established using ADAMS software. Kinematic simulations were then executed to verify the alignment between the simulated trajectory and theoretical predictions of the weeding cutter. Subsequently, single-factor simulations were performed on ADAMS, taking the weeding coverage rate as the assessment criterion. The main influencing factors were then determined, including operating speed, rotation speed of the weeding cutter, and preset angle threshold of information collection sensors. Taking the three main influencing factors obtained from the simulation as experimental factors, and the weed control rate as an evaluation index, a quadratic regression combination experiment was designed to construct a regression model of the weed control rate. Design Expert 8.0.6 software was utilized to optimize the parameters of the regression model. The optimal parameters were obtained as follows: operating speed of 300mm/s, rotation speed of the weeding cutter of 300 r/min, and preset angle threshold of 10°. The field validation experiment was obtained an average weed control rate of 91.24% under the optimal parameter settings, indicating the high accuracy of the model. This finding can provide a scientific basis to optimize the parameters of intra-row weeding machines for wolfberry and similar crops.