| Title | Optimal Parameters Design for Model Predictive Control using an Artificial Neural Network Optimized by Genetic Algorithm |
| Publication Type | Conference Paper |
| Year of Publication | 2021 |
| Authors | Yao, Chunxing, Sun, Zhenyao, Xu, Shuai, Zhang, Han, Ren, Guanzhou, Ma, Guangtong |
| Conference Name | 2021 13th International Symposium on Linear Drives for Industry Applications (LDIA) |
| Keywords | artificial neural network, Artificial neural networks, employee welfare, genetic algorithm, Inverters, Linear programming, Measurement, model predictive control, Predictive models, pubcrawl, Resiliency, Scalability, Switching frequency, T-type inverter, weighting factor design, work factor metrics |
| Abstract | Model predictive control (MPC) has become one of the most attractive control techniques due to its outstanding dynamic performance for motor drives. Besides, MPC with constant switching frequency (CSF-MPC) maintains the advantages of MPC as well as constant frequency but the selection of weighting factors in the cost function is difficult for CSF-MPC. Fortunately, the application of artificial neural networks (ANN) can accelerate the selection without any additional computation burden. Therefore, this paper designs a specific artificial neural network optimized by genetic algorithm (GA-ANN) to select the optimal weighting factors of CSF-MPC for permanent magnet synchronous motor (PMSM) drives fed by three-level T-type inverter. The key performance metrics like THD and switching frequencies error (ferr) are extracted from simulation and this data are utilized to train and evaluate GA-ANN. The trained GA-ANN model can automatically and precisely select the optimal weighting factors for minimizing THD and ferr under different working conditions of PMSM. Furthermore, the experimental results demonstrate the validation of GA-ANN and robustness of optimal weighting factors under different torque loads. Accordingly, any arbitrary user-defined working conditions which combine THD and ferr can be defined and the optimum weighting factors can be fast and explicitly determined via the trained GA-ANN model. |
| DOI | 10.1109/LDIA49489.2021.9505956 |
| Citation Key | yao_optimal_2021 |
Optimal Parameters Design for Model Predictive Control using an Artificial Neural Network Optimized by Genetic Algorithm