Nonlinear model predictive torque control of PMSMs for high performance applications. (December 2018)

• Record Type: Journal Article
• Authors: Englert, Tobias; Graichen, Knut
• Is Part Of: Control engineering practice. Volume 81(2018)
• Page Start: 43
• Page End: 54
• Publication Date: 2018-12
• Subjects: Nonlinear MPC -- Electric machine control -- Permanent magnet synchronous machine -- Nonlinear constraints -- Augmented Lagrangian method -- Gradient method -- Embedded optimization; Automatic control -- Periodicals; 629.89
• DOI: 10.1016/j.conengprac.2018.08.023
• Languages: English
• ISSNs: 0967-0661

Abstract

Abstract: 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 μ s.