Work

Safety-Constrained Disturbance-Compensated Model Predictive Control for Flexible-Joint Robots

Year: 2025

Type: article

Abstract: Flexible-joint robots (FJRs) offer safety and energy efficiency in collaborative tasks, yet achieving high-precision tracking remains challenging under strict state and safety constraints due to elastic coupling, model mismatch, and external disturbances. To address this issue, this paper proposes a safe and disturbance-compensated model predictive control (SDC-MPC) method that integrates model predictive control (MPC) with a disturbance observer (DOB) to estimate and compensate lumped uncertainties and disturbances in real time. To enforce safety, a control barrier function (CBF) is incorporated as an online inequality to maintain forward-invariance safety constraints. The method adapts safety margins to disturbances and allows soft relaxations of constraints when necessary, thereby ensuring feasibility under strong disturbances. A discrete-time implementation makes the approach suitable for real-time applications. Experiments on a single-joint platform demonstrate improved tracking performance and robustness.

Source: Applied Sciences

DOI: 10.3390/app152413238

Authors: Shiqi Cao, Fan Wang, Xin Li, Dalei Yao, Meilin Xie

Institutions: Chinese Academy of Sciences, Xi'an Institute of Optics and Precision Mechanics, University of Chinese Academy of Sciences

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

Funders: National Natural Science Foundation of China, National Science and Technology Major Project