An artificial neural network-based model predictive control of cascaded h-bridge multilevel inverter

Phuong Vu, Binh Bao Pho, Hoan Minh Tran, Minh Trong Tran

Abstract


In recent years, there has been an increasing interest in using Cascaded H-Bridge (CHB) for medium and high-power applications. Model predictive control (MPC) strategy has emerged as a promising option for controlling CHB with considerable advantages. However, the most significant disadvantage of MPC is the exponential increase of computational burden to solve the optimization, leading to an unacceptable amount of computing resources. Therefore, to overcome these difficulties, the ANN-MPC approach for CHB is proposed in this paper. Firstly, the multistep MPC controller is designed and operated in simulation environment to generate the data required for training. Secondly, after being successfully trained, the neural network can be used to control the system without the need for MPC to avoid the heavy-duty computing problem. The performance of ANN-MPC is evaluated and compared to that of conventional multistep MPC. Finally, a FPGA based ANN-MPC controller employing the trained ANN is designed to control the experimental system with three-phase five-level CHB with LC filter and linear loads. Both simulation and experimental results verified the excellent control performance of the proposed ANN-MPC.


Keywords


Model Predictive Control (MPC); Cascade H-Bridge (CHB); Artificial Neural Network (ANN); Field-Programmable Gate Array (FPGA)

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References


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DOI (PDF): https://doi.org/10.20508/ijrer.v12i3.13145.g8513

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