Data-driven adaptive decoupling control of induction motors using multi-model inversion

作者：Mei Cong Li^*; Yin Kai Ting; Huang Wen Tao; Liu Guo Hai

来源：Control and Decision, 2016, 31(6): 1037-1041.

DOI：10.13195/j.kzyjc.2015.0818

• Adaptive adjusting strategy
• Adaptive decoupling
• Affinity propagation
• Affinity propagation clustering
• Decoupling controls
• Multi model
• Operating condition
• Particle swarm optimization algorithm

To achieve high performance of control, a data-driven adaptive decoupling control method of induction motors using multi-model approach is proposed. In the method, local inverse models are trained on the clusters of simulation data by using the affinity propagation (AP) clustering method which can determine the number of clusters automatically, and membership functions are used to combine local models. For the problem that the parameters of induction motors usually change with operating conditions, an online adaptive adjusting strategy of the weights of local models is designed with the particle swarm optimization (PSO) algorithm to improve the adaptive ability of the proposed control method. Simulations show that the proposed method can achieve good performance of decoupling control with adaptive ability.

• 出版日期2016
• 单位江苏大学