A Robust Model Predictive Current Control Strategy with Improved Adaptive Quantum Genetic Algorithm-Based Parameter Identification for PMSM

The conventional model predictive current control (MPCC) for permanent magnet synchronous motor (PMSM) is affected by the issues of stator current fluctuation and control performance dependent on motor parameters. In order to solve the issues, a robust model predictive current control strategy with improved adaptive quantum genetic algorithm-based parameter identification (IAQGA-RMPCC) is proposed in this paper. First, the three non-adjacent effective voltage vectors are predicted and evaluated by the cost function, and two adjacent optimal effective voltage vectors are determined accurately and quickly. In addition, the dq-axis voltage error is considered. The optimal effective voltage vector action time is quickly calculated, the stator current fluctuation is reduced, and the robustness of motor parameters is enhanced. Furthermore, to address the effects of disturbances caused by variations in electrical parameters, an improved adaptive quantum genetic algorithm is proposed. Through improved quantum coding, more flexible population crossover and mutation is realized. The better populations and fitness are obtained in the early stages of the genetic algorithm. Through the design of the quantum revolving gate, the genetic algorithm is realized by converging more at later stage. Finally, the correctness and effectiveness of the IAQGA-RMPCC strategy are proved by the experimental results.