System Identification for Virtual Sensor-Based Model Predictive Control: Application to a 2-DoF Direct-Drive Robotic Arm

TL;DR

This paper introduces a framework that uses high-cost sensors during modeling to create virtual sensors, enabling effective nonlinear model predictive control of a robotic arm without expensive sensors during operation.

Contribution

The paper presents PVSID, a novel method for virtual sensor identification that facilitates NMPC in nonlinear systems with measurement constraints, validated on a robotic arm.

Findings

Virtual sensors enable precise tip trajectory tracking.

PVSID reduces reliance on costly sensors during operation.

Experimental validation shows effective control without motion capture.

Abstract

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.