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.

翻译：非线性模型预测控制为复杂非线性系统控制提供了一种强有力的方法，但仍面临两个关键挑战。首先，精确建模非线性动力学依然困难。其次，与控制目标直接相关的变量在运行期间往往无法直接测量。虽然在模型开发阶段可采用高成本传感器获取这些变量，但在实际部署中使用这些传感器通常不可行。为克服这些限制，我们提出了一种预测虚拟传感器辨识框架，该框架在建模阶段利用临时高成本传感器创建用于NMPC实施的虚拟传感器。我们在具有复杂关节交互作用的二自由度直驱机械臂上验证了PVSID框架，通过在建模阶段使用运动捕捉系统获取末端位置信息，并在NMPC中采用惯性测量单元。实验结果表明，采用辨识虚拟传感器的NMPC能够实现精确的末端轨迹跟踪，且无需在运行期间使用运动捕捉系统。PVSID为在关键变量测量受成本或操作限制的非线性系统中实施最优控制提供了一种实用解决方案。