3.1 Predictive Control Methods for Power Converters and Drives

Predictive control covers a very wide class of controllers that have found rather recent application in power converters. A classification for different predictive control methods is shown in Figure 3.1, as proposed in [1].

Figure 3.1 Classification of predictive control methods used in power electronics (Cortes et al., 2008 © IEEE)

The main characteristic of predictive control is the use of a model of the system for predicting the future behavior of the controlled variables. This information is used by the controller to obtain the optimal actuation, according to a predefined optimization criterion.

The optimization criterion in hysteresis-based predictive control is to keep the controlled variable within the boundaries of a hysteresis area [2], while in trajectory-based control the variables are forced to follow a predefined trajectory [3]. In deadbeat control, the optimal actuation is the one that makes the error equal to zero in the next sampling instant [4, 5]. A more flexible criterion is used in model predictive control (MPC), expressed as a cost function to be minimized [6].

The difference between these groups of controllers is that deadbeat control and MPC with continuous control set need a modulator in order to generate the required voltage. This will result in having a fixed switching frequency. The other controllers directly generate the switching signals for the converter, do not need a modulator, and will present a variable switching frequency.

One advantage of predictive control is that concepts are very simple and intuitive. Depending on the type of predictive control, implementation can also be simple, as with deadbeat control and finite control set MPC (especially for a two-level converter with horizon N = 1). However, some implementations of MPC can be more complex if the continuous control set is considered. Variations of the basic deadbeat control, in order to make it more robust, can also become very complex and difficult to understand.

Using predictive control it is possible to avoid the cascaded structure which is typically used in a linear control scheme, obtaining very fast transient responses. An example of this is speed control using trajectory-based predictive control.

Nonlinearities in the system can be included in the model, avoiding the need to linearize the model for a given operating point, and improving the operation of the system for all conditions. It is also possible to include restrictions on some variables when designing the controller. These advantages can be very easily implemented in some control schemes, such as MPC, but are very difficult to obtain in schemes like deadbeat control.

This book will focus on the application of MPC to power converters and drives, considering a finite control set and finite prediction horizon. More details will be found in the following chapters.