Model Predictive Control of Robot Flexible Joint Motor Based on Lyapunov Prediction Model

To address the parameter sensitivity issue of flexible joint permanent magnet synchronous motors (FJ-PMSMs) when employing a candidate voltage vector strategy, an adaptive method based on the Lyapunov function predictive model is proposed. By using the finite control set model predictive control (FCS-MPC) approach, the continuous input control law is transformed into relevant constraints of the FCS-MPC optimization problem. Specific equations are constructed through online adaptive laws to ensure system robustness, thereby tracking performance and minimizing current ripple. The effectiveness of the strategy is verified by experiments, the proposed strategy is compared with conventional MPC control methods. It effectively resolved the parameter uncertainties and load disturbance issues of the FJ-PMSM, showing superior current quality over traditional methods.