用虚拟传感器实现高精度机器人轨迹控制，无需昂贵设备实时运行
System Identification for Virtual Sensor-Based Model Predictive Control: Application to a 2-DoF Direct-Drive Robotic Arm
- 通过临时使用高成本传感器建模，生成可用于控制的虚拟传感器
- 在2自由度直驱机械臂上实现末端轨迹精确跟踪，误差极小
- 适合传感器受限的复杂非线性系统，如工业机器人控制
非线性模型预测控制（NMPC）虽能有效控制复杂非线性系统，但面临两大挑战：准确建模非线性动态困难，且关键控制变量常无法直接测量。尽管建模阶段可用高价传感器获取数据，但在实际部署中通常不可行。为此，本文提出预测型虚拟传感器识别（PVSID）框架，利用建模阶段的临时高成本传感器生成虚拟传感器，用于NMPC实施。我们在具有复杂关节耦合的两自由度直驱机械臂上验证该方法，建模时通过动作捕捉系统获取末端位置，控制时则使用惯性测量单元（IMU）。实验表明，采用识别出的虚拟传感器后，NMPC实现了无需动作捕捉系统的高精度末端轨迹跟踪。PVSID为测量受限的非线性系统提供了可行的最优控制实现方案。
原文摘要 · Abstract (English)
Nonlinear Model Predictive Control (NMPC) offers a powerful approach for controlling complex nonlinear systems, yet faces two key challenges. First, accurately modeling nonlinear dynamics remains difficult. Second, variables directly related to control objectives often cannot be directly measured during operation. Although high-cost sensors can acquire these variables during model development, their use in practical deployment is typically infeasible. To overcome these limitations, we propose a Predictive Virtual Sensor Identification (PVSID) framework that leverages temporary high-cost sensors during the modeling phase to create virtual sensors for NMPC implementation. We validate PVSID on a Two-Degree-of-Freedom (2-DoF) direct-drive robotic arm with complex joint interactions, capturing tip position via motion capture during modeling and utilize an Inertial Measurement Unit (IMU) in NMPC. Experimental results show our NMPC with identified virtual sensors achieves precise tip trajectory tracking without requiring the motion capture system during operation. PVSID offers a practical solution for implementing optimal control in nonlinear systems where the measurement of key variables is constrained by cost or operational limitations.
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