標題: Chaotic Newton-Raphson Optimization Based Predictive Control for Permanent Magnet Synchronous Motor Systems with Long-Delay
作者: Wu, Bing-Fei
Lin, Chun-Hsien
電機資訊學士班
Undergraduate Honors Program of Electrical Engineering and Computer Science
關鍵字: predictive control;neural network;Newton-Raphson;chaos optimization algorithm;long-delay plant
公開日期: 1-一月-2015
摘要: A Tent-map chaotic Newton-Raphson optimization based neural network predictive control (TCNR-NPC) is developed to apply to the long-delay permanent magnet synchronous motor (PMSM) system in this paper. Due to a nonlinear model utilized in the predictive controller, nonlinear optimization methods turn into an important issue. To overcome the shortcoming of the conventional nonlinear programming on the initial condition sensitivity and maintain the accuracy of optimal solution, chaos optimization algorithm (COA) and Newton-Raphson (NR) are combined. With the comparison of COA and NR based optimization methods, our approach, the Tent-map chaotic Newton-Raphson (TCNR) optimization, is easier to reach the global optimum; thus, it would be employed in neural network predictive control. It is found that TCNR-NPC has a better performance than those of GPC, modified GPC, adaptive extended PSO based NPC, and PSO based PI controllers in real experiments.
URI: http://dx.doi.org/10.1109/SMC.2015.78
http://hdl.handle.net/11536/129824
ISBN: 978-1-4799-8696-5
ISSN: 1062-922X
DOI: 10.1109/SMC.2015.78
期刊: 2015 IEEE INTERNATIONAL CONFERENCE ON SYSTEMS, MAN AND CYBERNETICS (SMC 2015): BIG DATA ANALYTICS FOR HUMAN-CENTRIC SYSTEMS
起始頁: 382
結束頁: 387
顯示於類別:會議論文