Design and Testing of High-quality Tea Picking System Based on Adaptive Model Compensation Control

Abstract:

Intelligent picking is a key development for improving the efficiency of the tea industry. Aiming to address the current issues of poor positioning accuracy and stability of mechanical arms used for picking high-quality tea, an adaptive model compensation control strategy was proposed. Firstly, a mechanical arm dynamics model was constructed by using the Lagrange method. Secondly, a radial basis function (RBF) neural network and a nonlinear disturbance observer (NDO) were designed to adaptively compensate for the dynamic model errors and external disturbances, respectively. Furthermore, the stability of the proposed control system was proven based on Lyapunov stability theory. Simulation test results showed that, following the introduction of the nonlinear disturbance observer, the position and velocity tracking performance of the three-axis robotic arm improved significantly, with position tracking errors reduced to 0. 18 rad, 0. 01 rad, and 0. 43 rad, and fluctuation amplitudes noticeably decreased. Field picking validation tests showed that, compared with traditional PID control, this strategy reduced the robotic arm??s acceleration fluctuations by 86. 8% , reduced vibration amplitude by 94. 8% , achieved an average picking time of approximately one second per cycle and a successful picking rate of 51. 82% , as well as an optimal picking speed of 0. 79 s per tea bud. The research successfully overcame the challenges of high-precision positioning and smooth motion coordination control during tea bud picking, offering a reliable technical solution for the intelligent picking of premium tea.