Adaptive Model-Free Deadbeat Predictive Current Control for Linear Permanent Magnet Synchronous Motor Based on Immune Algorithm

The model free deadbeat predictive current control (MFDPCC) for linear permanent magnet synchronous motor (LPMSM) drive system is emerging as a promising method due to its straightforward structure and strong parameter robustness. However, the conventional MFDPCC controller with the fixed gain leads to a contradiction between the dynamic and steady-state performance. To overcome this limitation, an adaptive model free deadbeat predictive current control based on immune algorithm (AMFDPCC-IA) is proposed in this article. First, the impact of the parameters mismatch on traditional DPCC is analyzed. Then, an ultra-local model, known as a typical branch of model free control, is developed to replace the conventional LPMSM model. For the ultra-local model with a typical fixed gain, the control performance is analyzed in detail, and it points out that there exits the contradiction between the dynamic and steady-state performance. Thus, to effectively balance the conflict, the immune algorithm is introduced to optimize the fixed parameter adaptively. Finally, the experimental test was carried out on a 0.9-kW LPMSM test bench, and the experimental results verify the effectiveness of the proposed method.