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In this work, an offset-free continuous control set model predictive current control (CCS-MPCC) strategy for synchronous machines based on a slack formulation of the Primal-Dual Interior-Point method is proposed. A horizon of two steps is achieved within 100 µs sampling period. To account for robustness against model mismatch and uncertainty, an incremental formulation of the MPC problem is used to ensure zero steady-state tracking error. The proposed controller is compared with the state of the art Field Oriented Control with PI controllers (FOC-PI), with the Deadbeat Model Predictive Current Control (DB-MPCC), and with the latter controller combined with discrete integrators in the feedback loop (DB-MPCC-I). Experimental results on a 0.5 kW PMSM prove that the proposed CCS-MPCC has outperformed the state of the art control techniques typically used to control electrical machines.

In this work, a data-driven model predictive control (MPC) approach for the current control of synchronous machines is presented. The modelof 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 model11The term first-principles model is used throughout the paper to call the machine model derived from the physical laws governing the functionality of the synchronous motor, whether it is the mostly used LTI model or the nonlinear model with the inductance/flux current-dependant maps.of the machine.

Der Beitrag beschreibt die Modellbildung, Analyse und Bewegungsplanung für das BionicKangaroo, das im Rahmen des Festo Bionic Learning Networks entwickelt wurde. Zunächst wird auf die Modellierung eingegangen und die prinzipielle Machbarkeit und Stabilisierbarkeit des Hüpfzyklus anhand eines modellprädiktiven Regelungsansatzes numerisch untersucht. Der eigentliche Reglerentwurf gliedert sich in die modellbasierte Regelung der Flug- und Bodenphase. Eine zusätzliche Schaltbedingung verbindet die Flug- und Bodenphase miteinander und initiiert den nächsten Hüpfzyklus durch das Abdrücken vom Boden.