Parameter Estimation for PMSM based on a Back Propagation Neural Network Optimized by Chaotic Artificial Fish Swarm Algorithm

Autor: Shilang Zhu, Jiang Jianwu, Tao Peng, Zhi Chen, Wang Yihuai, Lianmin Shi
Rok vydání: 2020
Předmět:
Zdroj: INTERNATIONAL JOURNAL OF COMPUTERS COMMUNICATIONS & CONTROL. 14:615
ISSN: 1841-9844
1841-9836
DOI: 10.15837/ijccc.2019.6.3705
Popis: Permanent Magnet Synchronous Motor(PMSM) control system with strong nonlinearity makes it difficult to accurately identify motor parameters such as stator winding, dq axis inductance, and rotor flux linkage. Aiming at the premature convergence of traditional Back Propagation Neural Network(BPNN) in PMSM motor parameter identification, a new method of PMSM motor parameter identification is proposed. It uses Chaotic Artificial Fish Swarm Algorithm(CAFSA) to optimize the initial weights and thresholds of BPNN, and then strengthens training by BPNN algorithm. Thus, the global optimal network parameters are obtained by using the global optimization of CAFSA and the local search ability of BPNN. The simulation results and experimental data show that the initial value sensitivity of the network model optimized by CAFS-BPNN Algorithm is weak, the parameter setting is robust, and the system stability is good under complex conditions. Compared with other intelligent algorithms, such as RSL and PSO, CAFS-BPNNA has high identification accuracy and fast convergence speed for PMSM motor parameters.
Databáze: OpenAIRE