用滑模控制实现无人实验室中危险品的平稳精准操作
Autonomous Manipulation of Hazardous Chemicals and Delicate Objects in a Self-Driving Laboratory: A Sliding Mode Approach
- 基于模型的滑模控制，用双曲正切函数优化机械臂运动
- 相比传统方法，控制能耗降低90%，轨迹跟踪更平滑
- 适合需要高精度与安全性的智能移动机械臂系统
在自驱动化学实验室内，机器人系统需精确操控化学仪器与材料。本文提出一种基于模型的滑模控制（MBSMC），采用双曲正切函数调节安装于移动平台上的机械臂运动，专为搬运装有危险化学品的易碎玻璃器皿设计，旨在减少突变、实现柔和精准的轨迹跟踪。通过关节和笛卡尔空间多维度指标，对比了非模型滑模控制（NMBSMC）与比例-积分-微分（PID）控制器。结果表明，相较于PID与NMBSMC，MBSMC显著提升了运动平滑性，控制努力最多降低90%，验证了其在应对不确定性与外部扰动时的鲁棒性与精度。实验成功完成容器抓取与窗体操作等任务，而PID因难以处理非线性动态与干扰导致严重轨迹误差而失败。结果证实该控制器可有效支持智能移动机械臂在自主实验室环境中的平稳、精确与安全运行。
原文摘要 · Abstract (English)
Precise handling of chemical instruments and materials within a self-driving laboratory environment using robotic systems demands advanced and reliable control strategies. Sliding Mode Control (SMC) has emerged as a robust approach for managing uncertainties and disturbances in manipulator dynamics, providing superior control performance compared to traditional methods. This study implements a model-based SMC (MBSMC) utilizing a hyperbolic tangent function to regulate the motion of a manipulator mounted on a mobile platform operating inside a self-driving chemical laboratory. Given the manipulator's role in transporting fragile glass vessels filled with hazardous chemicals, the controller is specifically designed to minimize abrupt transitions and achieve gentle, accurate trajectory tracking. The proposed controller is benchmarked against a non-model-based SMC (NMBSMC) and a Proportional-Integral-Derivative (PID) controller using a comprehensive set of joint and Cartesian metrics. Compared to PID and NMBSMC, MBSMC achieved significantly smoother motion and up to 90% lower control effort, validating its robustness and precision for autonomous laboratory operations. Experimental trials confirmed successful execution of tasks such as vessel grasping and window operation, which failed under PID control due to its limited ability to handle nonlinear dynamics and external disturbances, resulting in substantial trajectory tracking errors. The results validate the controller's effectiveness in achieving smooth, precise, and safe manipulator motions, supporting the advancement of intelligent mobile manipulators in autonomous laboratory environments.
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