Cascade Model Predictive Control of Permanent Magnet Synchronous Motor for Industrial Robot

With the development of industrial automation and robotics, the permanent magnet synchronous motor (PMSM) system, as the actuator of industrial robots, often works in high-precision occasions with strict requirements for control accuracy. At present, the traditional vector control and direct torque control are difficult to meet the high-performance control objectives. As an advanced control strategy, model predictive control (MPC) is easy to realize optimal control. Model predictive current control eliminates the integral influence of current proportional integral controller and improves the dynamic response effect of current. However, its speed outer loop generally still adopts speed proportional integral controller, resulting in the unsatisfactory dynamic effect of motor speed. At present, most of the research on MPC of PMSM is carried out for a certain link of the control system, and the advantages of MPC performance are not given full play. Therefore, this paper applies the MPC strategy to the current control and speed control of PMSM for industrial robots to improve the dynamic and static performance of the system.