Nonlinear model predictive torque control of PMSMs for high performance applications

This contribution presents a nonlinear model predictive control (MPC) scheme for the torque control of permanent magnet synchronous machines. The control scheme is based on the nonlinear dq-model including current-dependent inductivities and provides a desired torque in an energy-efficient way while accounting for constraints on the DC link current, phase currents, and hexagonal voltage constraints. The MPC algorithm uses an augmented Lagrangian method in combination with a real-time gradient method to allow for a computationally efficient solution. Experimental results for a standard industrial drive show the performance, robustness, and computational efficiency of the MPC with a sampling time of 500 mu s.