Torque- Robust Model Predictive Control for Robotic Joints with Harmonic Reducers

Robotic joint control in systems with harmonic reducers encounters challenges in dynamic torque scenarios that outstrip the capabilities of traditional proportional-integral (PI) controllers. Although effective in numerous situations, PI controllers may fall short in ensuring torque robustness and position accuracy under dynamic torque disturbances. This paper introduces a torque-robust dual-stage predictive control frame-work (TRDS-PCF), leveraging model predictive control (MPC) to address these challenges. The TRDS-PCF refines conventional control methods by incorporating a dual-layer predictive strategy alongside a harmonic reducer compensation module, significantly enhancing control accuracy and responsiveness. Validation through simulation studies demonstrates the TRDS-PCF's superior performance, evidencing substantial reductions in adjustment times without overshoot for no-load scenarios and sustained robustness under variable torque conditions. This development highlights the TRDS-PCF's potential to improve the performance and reliability of robotic systems substantially.