基于icpso算法的异步电动机参数辨识与控制分析based on the icpso asynchronous motor parameters identification and control analysis of the algorithm.docx

Abstract
With the constant development of the vector control theory and technology, AC electric drive systems have dominated in the field of high-performance electric drive. When the demand for the control performance of vector control system is enhanced, the parameters time-variety of induction motor and the sensitivity of vector control system to motor parameters variation have e problems. Both off-line and on-line existing parameter identification methods of induction motor, cannot meet the requirement of vector control system on real time and convergence. Therefore, researching an online induction motor parameters identification algorithm, and improving vector control systems, is of great significance for further enhancing performance of AC electric drive systems. In view of these problems, this article studied on online induction motor parameters identification method and its application in vector control systems.
Integrated with the advantages of the standard particle swarm algorithm (SPSA), the thought of co-evolutionary and immune clone selection algorithm (CSA), an immune co-evolutionary particle swarm optimization (ICPSO) algorithm was proposed. In this algorithm, the population is divided into several general populations and one dominant population. SPSA is used in the general populations with neighbor information, while CSA is used in dominant population to accelerate convergence of dominant individuals, and the thought of puting and the interactions between species in co-evolutionary algorithm is borrowed in ICPSO.
By adopting the superiority of ICPSO algorithm in wide range search and dynamic target optimization, the problem of online identification of asynchronous motor parameters has been solved. Based on original vector control system, with the results of online identification, the parameters of the flux observation, the decoupling controller and the PI controllers have been adjusted. A novel vector control system with parameters identification b