Koopman Operator-Based Finite-Control-Set Model Predictive Control for Electrical Drives

Abstract

Predictive control of power electronic systems always requires a suitable model of the plant. Using typical physics-based white box models, a trade-off between model complexity (i.e. accuracy) and computational burden has to be made. This is a challenging task with a lot of constraints, since the model order is directly linked to the number of system states. Even though white-box models show suitable performance in most cases, parasitic real-world effects often cannot be modeled satisfactorily with an expedient computational load. Hence, a Koopman operator-based model reduction technique is presented which directly links the control action to the system's outputs in a black-box fashion. The Koopman operator is a linear but infinite-dimensional operator describing the dynamics of observables of nonlinear autonomous dynamical systems which can be nicely applied to the switching principle of power electronic devices. Following this data-driven approach, the model order and the number of system states are decoupled which allows us to consider more complex systems. Extensive experimental tests with an automotive-type permanent magnet synchronous motor fed by an IGBT 2-level inverter prove the feasibility of the proposed modeling technique in a finite-set model predictive control application.

References in corpus (5)

• Linear predictors for nonlinear dynamical systems: Koopman operator meets model predictive control
• On Convergence of Extended Dynamic Mode Decomposition to the Koopman Operator
• Koopman operator-based model reduction for switched-system control of PDEs
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Cited by in corpus (5)

• Vehicular Applications of Koopman Operator Theory -- A Survey
• Feedback control of nonlinear PDEs using data-efficient reduced order models based on the Koopman operator
• Data-driven feedback stabilization of nonlinear systems: Koopman-based model predictive control
• Learning Koopman Representations for Hybrid Systems
• Frequency Control of Decoupled Synchronous Machine Using Koopman Operator Based Model Predictive