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

TL;DR

This paper introduces a data-driven Koopman operator-based model reduction technique for finite-set model predictive control of electrical drives, enabling more complex system modeling with reduced computational load.

Contribution

It presents a novel application of Koopman operator theory for model reduction in power electronic control systems, improving accuracy and computational efficiency.

Findings

Successful experimental validation on a PMSM drive system.

Enhanced control performance with reduced model complexity.

Feasibility demonstrated in automotive inverter applications.

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.