基于交互力约束的无源遥操作混合线性预测控制

Nicola Piccinelli, R. Muradore
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引用次数: 1

摘要

双边远程操作技术允许人类操作员与远程环境进行交互,通常应用于安全关键场景。在这种情况下,即使力反馈允许感知与环境的交互,通信延迟也可能导致损害或副作用。例如,在像手术这样的关键场景中,不同的组织可以承受不同的最大力,并且它们的操作通常需要力控制。模型预测控制(MPC)由于能够在优化问题中嵌入约束条件而成为解决这种情况的一种方法。特别是线性MPC,由于其具有较高的控制频率,通常用于实时控制。在本文中,我们提出了一种基于混合mpc的双边远程操作。混合系统允许显式地处理相互作用力的切换行为。这种方法使自由运动和接触状态之间的切换次数最小化。这种减少的抖振提供了机械臂在软接触和硬接触情况下的平滑行为。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Passivity-Based Teleoperation With Interaction Force Constraints Using Hybrid Linear Model Predictive Control
The bilateral teleoperation technique allows a human operator to interact with a remote environment and is generally applied in safety-critical scenarios. In such scenarios, even if the force feedback allows to feel the interaction with the environment, communication delay can lead to damages or side effects. For instance, in a critical scenario like surgery, different tissues tolerate different maximum forces and their manipulation often requires force control. Model Predictive Control (MPC) could be a solution to such situations thanks to its capability of embedding constraints in the optimization problem. In particular, linear MPC is usually employed in real-time control since it allows to have high control frequency. In this paper, we propose a hybrid-MPC based bilateral teleoperation. The hybrid system allows to explicitly handle the switching behaviour of the interaction force. Such methodology minimizes the number of switches between free motion and contact states. This reduced chattering provides smooth behaviour of the manipulator both in soft and hard contact scenarios.
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