基于自适应模型补偿控制的名优茶采摘系统设计与试验

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

摘要:

智能化采摘是提升茶叶产业效率的关键发展方向。针对当前名优茶采摘机械臂存在的定位精度不足和稳定性欠佳问题,本研究提出了一种自适应模型补偿的控制策略。首先,采用拉格朗日方法构建了机械臂动力学模型;其次,设计了径向基函数(Radialbasisfunction,RBF)神经网络和非线性扰动观测器(Nonlineardisturbanceobserver,NDO),分别用于逼近机械臂动力学模型误差和估计补偿系统外部干扰与未建模误差;进而,基于Lyapunov稳定性理论证明了所提出的控制系统具有稳定性。仿真试验结果表明:引入非线性干扰观测器后,三轴机械臂的位置与速度跟踪性能显著提升,位置跟踪误差降低至0.18、0.01、0.43rad,波动幅度明显降低。田间采摘验证试验表明,相比传统PID控制,该策略使机械臂运动加速度波动降低86.8%,振动幅度减小94.8%,单棵茶芽平均采摘耗时约为1.00s,最大成功采摘率达到51.82%,单棵茶芽最优采摘时间达0.79s。本文研究有效解决了茶芽采摘过程中的高精度定位与运动平稳协同控制难题,为名优茶智能化采摘提供了可靠的技术方案。

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.