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

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.