# Model Predictive Control Algorithm for Converter Based on a Convolutional Neural Network
## 1 Introduction  
Model predictive control (MPC) is an optimization control strategy based on a dynamic model of a system. It improves the system's dynamic performance and stability by employing real-time prediction of future states and performing rolling optimization of the control sequences [1, 2]. Due to its core advantages of coordinating multi-variable constraints and flexibly designing objective functions, MPC has been extensively applied in converter control systems, such as three-phase inverters with output LC filters [3, 4, 5]. Finite control set model predictive control (FCSMPC), as the main form of converter MPC algorithms, achieves comprehensive optimization control by leveraging the discrete nature of converter switching states [6, 7, 8]. However, the primary drawbacks of conventional FC-SMPC algorithms are as follows: (1) single-step prediction calculations lead to conservative algorithms, (2) model mismatch or parameter drift generates prediction errors, (3) difficulty in calculating weighting factors in cost functions, and (4) variable switching frequencies. To overcome these challenges, the academic community has proposed multiple improvement approaches. In [9, 10], a delay compensation strategy is proposed, which effectively alleviates the effects of computational latency on current-tracking performancein three-phase inverter systems. Due to there being a prediction error between the actual output value of the converter at any moment and the prediction value obtained by the constructed prediction model [11; 12; 13], which affects the control performance of the converter, in [14], an FCSMPC with feedback correction scheme is introduced, which utilizes the model prediction error at the previous moment to adjust the prediction model output at the current moment, thus achieving closed-loop correction of the prediction model output and reducing the error accumulation effect. In order to reduce the conservatism of the FCSMPC algorithm, a multi-step prediction computation framework is designed to realize three-step prediction computation in [15]. To solve the problem of accuracy loss of the system model in the FCSMPC algorithm, in [16] an FCS model-free predictive control framework is constructed to estimate the system dynamic characteristics online with the help of a low-pass filter, which enhances the robustness to model adaptation. In [17; 18], MPC is combined with sliding mode control (SMC) to enhance the system model accuracy and thus improve the system stability and control performance. Nevertheless, existing approaches still face many challenges including strong dependence on model accuracy and the complex calculation of multi-objective weighting factors. Furthermore, any improvement strategies for FCSMPC algorithms inevitably increase the computational burden, so it is impossible to synthesize all the improvement strategies to achieve the optimal FCSMPC scheme. In recent years, neural networks have provided new research ideas for converter control due to their powerful nonlinear mapping capabilities and numerical modeling characteristics. This strength enables the ability to learn system dynamic characteristics from data, tune parameters adaptively, and handle complex time-varying problems. Artificial neural network (ANN)-based controllers can learn system dynamic features by modeling through a black box, offering a viable option for online implementation of complex control algorithms. In [19; 20; 21; 22; 23], an ANN is adopted to approximate the external characteristics of MPC controllers, reducing real-time computation of the algorithms and enabling the implementation of more accurate higher-order system models. In [24; 25], a constrained modulated MPC method is proposed, where an ANN simplifies switching-state evaluation procedures to achieve efficient constraint handling.