Real-Time Capable Model Predictive Control of Permanent Magnet Synchronous Motors Using Particle Swarm Optimisation
Conference: PCIM Europe 2016 - International Exhibition and Conference for Power Electronics, Intelligent Motion, Renewable Energy and Energy Management
05/10/2016 - 05/12/2016 at Nürnberg, Deutschland
Proceedings: PCIM Europe 2016
Pages: 8Language: englishTyp: PDF
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Authors:
Wallscheid, Oliver; Boecker, Joachim (Paderborn University, Power Electronics and Electrical Drives, 33095 Paderborn, Germany)
Ammann, Ulrich (Esslingen University of Applied Sciences, 73037 Göppingen, Germany)
Abstract:
Permanent magnet synchronous motors (PMSM) are widely used in many industrial applications due to their attractive power and torque densities. To fully exploit their torque dynamics, non-linear model predictive control (MPC) approaches have been investigated in the last years. Besides the requirement of an accurate motor model solving a suitable cost function under real-time conditions is a challenging task, since highly-utilised PMSM exhibit a strongly non-linear behaviour due to (cross-)saturation effects. In this contribution a particle swarm optimisation (PSO) based MPC using multiple sub-swarms which are implemented on a multi-core processor platform is investigated. Thanks to parallel program execution, a suitable turnaround time (<100 mus) is achieved. In contrast to conventional MPC approaches the proposed PSO-based technique is not limited to convex cost functions or linear plant models and therefore extends the MPC application scope. Measurement results based on a 60 kW prototype motor fed by a 2-level-IGBT inverter demonstrate the general proof of concept. In comparison to a state-ofthe-art linear feedback controller (PI-type) a slightly improved control performance is observed.