自动化学报2012，Vol.38Issue(10)：1698-1708,11.DOI:10.3724/SP.J.1004.2012.01698
免疫协同微粒群进化算法的永磁同步电机多参数辨识模型方法
Immune Co-evolution Particle Swarm Optimization for Permanent Magnet Synchronous Motor Parameter Identification
摘要
Abstract
Aiming at the problem of permanent magnet synchronous motor (PMSM) multiple parameter identification, a novel parameter identification approach to PMSM based on immune co-evolution particle swarm optimization (ICPSO) algorithm is proposed. The proposed ICPSO consists of one memory subpopulation and several normal subpopulations. In each generation of the algorithm, the best individual of each normal subpopulation will be memorized into the memory population. A hybrid method, such as elitist reservation, immune network, cauchy mutation, etc., which creates new individuals by using three different operators, is proposed to ensure the diversity of all the normal subpopulations. Furthermore, a simple wavelet learning operator is employed for accelerating the convergence speed of pbest. The immune clonal selection operator is employed for optimizing the memory population while the migration scheme is employed for the information exchange between memory and normal subpopulations. Its performance is further verified by its application in multi-parameter estimation of permanent magnet synchronous machines, which shows that its performance is much better than other PSOs in simultaneously estimating the machine dq-axis inductances, stator winding resistance, and rotor flux linkage. In addition, it can also effectively track the varied parameter.关键词
人工免疫系统/粒子群优化/协同进化/永磁同步电机/参数辨识Key words
Aartificial immune system (AIS)/ particle swarm optimization (PSO)/ co-evolutionary/ permanent magnet synchronous motors (PMSM)/ parameter identification引用本文复制引用
刘朝华,章兢,李小花,张英杰..免疫协同微粒群进化算法的永磁同步电机多参数辨识模型方法[J].自动化学报,2012,38(10):1698-1708,11.基金项目
国家自然科学基金(61174140),教育部高校博士点基金(20110161110035)资助 （61174140）
