Work

Fusing Phase Map Servoing and MPC for High-Precision Robotic Tracking of Dynamic Objects

Year: 2026

Type: article

Abstract: This paper presents a unified framework for high-precision dynamic target tracking that combines phase-map-based visual servoing with Model Predictive Control (MPC). Phase maps obtained from fringe projection provide dense, subpixel geometric feedback, enabling accurate end-effector velocity computation; however, their high dimensionality leads to substantial computational overhead that hinders real-time control. To overcome this limitation, we introduce a phase-map-specific dimensionality reduction strategy that constructs a low-dimensional control subspace through gradient-guided sparsification and PCA embedding while preserving the controllability of the original interaction model. An adaptive prediction horizon is further developed to regulate MPC complexity according to the rate of phase variation, enabling real-time deployment without compromising tracking accuracy. In addition, an Extended Kalman Filter (EKF) is integrated into the control loop to compensate for system delays and improve trajectory prediction in dynamic scenarios. Experimental results on multi-axis robotic manipulation demonstrate that the proposed approach achieves superior tracking accuracy and computational efficiency compared with conventional visual servoing methods, validating the feasibility of phase-map-driven predictive control for high-speed dynamic target tracking.

Source: Actuators

DOI: 10.3390/act15020077

Authors: Qinghui Zhang, Tianhao Han, Lei Lu, Wei Pan, Ge Gao

Institution: Henan University of Technology

Language: English

Topic: Advanced Control Systems Optimization

Subfield: Control and Systems Engineering

Field: Engineering

Domain: Physical Sciences

Open Access status: gold

Estimated APC paid: $2,896

Funder: National Natural Science Foundation of China