Artificial Neural Network Based Optimal Feedforward Torque Control of Interior Permanent Magnet Synchronous Machines: A Feasibility Study and Comparison with the State-of-the-Art

Max A. Buettner, Niklas Monzen and Christoph M. Hackl
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Max A. Buettner: Department of Electrical Engineering and Information Technology, Hochschule München (HM) University of Applied Sciences, Lothstr. 64, 80335 München, Germany
Niklas Monzen: Department of Electrical Engineering and Information Technology, Hochschule München (HM) University of Applied Sciences, Lothstr. 64, 80335 München, Germany
Christoph M. Hackl: Department of Electrical Engineering and Information Technology, Hochschule München (HM) University of Applied Sciences, Lothstr. 64, 80335 München, Germany

Energies, 2022, vol. 15, issue 5, 1-38

Abstract: A novel Artificial Neural Network (ANN) Based Optimal Feedforward Torque Control (OFTC) strategy is proposed which, after proper ANN design, training and validation, allows to analytically compute the optimal reference currents (minimizing copper and iron losses) for Interior Permanent Magnet Synchronous Machines (IPMSMs) with highly operating point dependent nonlinear electric and magnetic characteristics. In contrast to conventional OFTC, which either utilizes large look-up tables (LUTs; with more than three input parameters) or computes the optimal reference currents numerically or analytically but iteratively (due to the necessary online linearization), the proposed ANN-based OFTC strategy does not require iterations nor a decision tree to find the optimal operation strategy such as e.g., Maximum Torque per Losses (MTPL), Maximum Current (MC) or Field Weakening (FW). Therefore, it is (much) faster and easier to implement while (i) still machine nonlinearities and nonidealities such as e.g., magnetic cross-coupling and saturation and speed-dependent iron losses can be considered and (ii) very accurate optimal reference currents are obtained. Comprehensive simulation results for a real and highly nonlinear IPMSM clearly show these benefits of the proposed ANN-based OFTC approach compared to conventional OFTC strategies using LUT-based, numerical or analytical computation of the reference currents.

Keywords: electrical drive control system; operation management; optimal feedforward torque control; optimal reference current computation; transformer-like nonlinear machine model; artificial neural network; synchronous motor; interior permanent magnet synchronous machine; machine learning (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2022
References: View complete reference list from CitEc
Citations: View citations in EconPapers (4)

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