This paper proposes a new finite control set model predictive control (FCS-MPC) strategy to improve coordination of the permanent magnet direct-drive motors (PMDDM) in humanoid robotic arm shoulders. First, the predicting plant model (PPM) used for prediction is established. Considering that the speed property is not directly related to the control voltages, the speed prediction model is established based on integral approximation. Second, an on-demand-mode evaluation scheme based on the concept of “priority” is developed to establish the cost function. Unlike traditional PI-based methods, the proposed FCS-MPC method incorporates both speed and current into one regulator. This can reduce the number of control loops and improve the response speed of the whole system. Finally, the comparative experiment is conducted on a three-phase PMDDMs drive to validate the proposed FCS-MPC strategy.
