Predictive Control of Power Converters and Electrical Drives

11.1 Introduction

One of the major advantages of model predictive control (MPC) is that several control targets, variables, and constraints can be included in a single cost function and simultaneously controlled. In this way typical variables such as current, voltage, torque, or flux can be controlled while achieving additional control requirements like switching frequency reduction, common-mode voltage reduction, and reactive power control, to name just a few. This can be accomplished simply by introducing the additional control targets in the cost function to be evaluated. However, the combination of two or more variables in a single cost function is not a straightforward task when they are of a different nature (different units and different orders of magnitude in value). Each additional term in the cost function has a specific weighting factor, which is used to tune the importance or cost of that term in relation to the other control targets. These parameters have to be properly designed in order to achieve the desired performance. Unfortunately, there are no analytical or numerical methods or control design theories to adjust these parameters, and currently they are determined based on empirical procedures. Although this challenge has not prevented MPC from being applied successfully to several power converters, it is highly desirable to establish a procedure or define some basic guidelines to reduce the uncertainty and improve the effectiveness of the tuning stage.

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