In this work, a data-driven model predictive control (MPC) approach for the current control of synchronous machines is presented. The model of the motor is represented via a long-short term memory (LSTM) neural network (NN). The model is obtained purely from collected data and doesn't include any physical knowledge. As an online optimization using the obtained data-driven model is not easily implementable in the available sampling time, the neural model is used to solve an MPC problem offline. Finally, the control policy is learned via another computationally implementable NN that runs in real-time as a current controller. The proposed data-driven MPC controller is tested experimentally, and is bench-marked against MPC schemes that incorporate the well-known physically-based first-principles linear and nonlinear model of the machine.     «

In this work, a data-driven model predictive control (MPC) approach for the current control of synchronous machines is presented. The model of the motor is represented via a long-short term memory (LSTM) neural network (NN). The model is obtained purely from collected data and doesn't include any physical knowledge. As an online optimization using the obtained data-driven model is not easily implementable in the available sampling time, the neural model is used to solve an MPC problem offline. F...     »