# Model Predictive Control Algorithm for Converter Based on a Convolutional Neural Network
## 2 Neural Network Controller for Converters and Model Switching Strategy
### Neural Network Switching Strategies  
To address the above requirement for switching neural network model parameters, an MPC system structure for converters based on a neural network, as shown in Figure 4, is constructed. This system provides information for switching neural network controllers through online identification of the converter load types. When the load type of the converter changes, the CNN analyzes the load type, and then the control model switches to the corresponding BP control model parameters based on the identified load type. In Figure 4, the identifiable load types include the following: no-load (NL), high-power-factor resistive-inductive load (HPFRIL), low-power-factor resistive-inductive load (LPFRIL), and nonlinear load (NLL), numbered as 1, 2, 3, and 4, respectively, for switching corresponding neural network controller parameters.  
The switching rules for these neural network controllers are illustrated in Figure 5, with specific control strategies comprising the following: the classical converter FC-SMPC algorithm (Classical FCSMPC), FCSMPC algorithm with prediction error compensation (Compensated FCSMPC), and FCSMPC algorithm integrating prediction error compensation and multi-step prediction strategy (Optimal FCSMPC) [14; 15]. The system operates as follows: initially, the Classical FCSMPC algorithm is first used to start the converter system operation. When the CNN identifies the load type, the system switches to the Compensated FCSMPC neural network controller corresponding to the identified load. When a decline in control performance is detected, the system switches to the Optimal FCSMPC neural network controller corresponding to the identified load. If further performance degradation is detected, the system reverts to the Classical FCSMPC algorithm to maintain stable converter operation. The neural network controller (NNC) set constructed in this article includes NNC (NL--Classical  
Figure 3: Variation curve of system tracking control error.  
FCSMPC), NNC (HPRFIL--Compensated FCSMPC), NNC (LPFRIL--Compensated FC-SMPC), NNC (NLL--Compensated FCSMPC), NNC (HPFRIL--Optimal FCSMPC), NNC (LPFRIL--Optimal FCSMPC), and NNC (NLL--Optimal FCSMPC). When the converter system operates under the control of either the Compensated FCSMPC or Optimal FCSMPC neural network controllers, it switches to the corresponding neural network controller with the same control strategy if a load variation is detected. When the identified load type is NL, the system uniformly switches back to the Classical FCSMPC algorithm for converter control. The above switching process is based on the correct identification of the load type. When the identified load type is wrong, the parameters of the neural network controller after system switching do not match with the current load type, the control performance is bound to deteriorate, and the larger tracking control error triggers a new round of load-type identification until the correct load type is identified and switched to the corresponding neural network controller.  
In fact, in order to improve the accuracy of load-type identification, we adopt multiple identification measures in the following simulation experiments. When the identification results are consistent three times within a certain period of time (such as within 1 s), the final identification result is output, which ensures the accuracy of the identification.