华南理工大学学报(自然科学版) ›› 2010, Vol. 38 ›› Issue (3): 118-122.doi: 10.3969/j.issn.1000-565X.2010.03.021

• 机械工程 • 上一篇    下一篇

激光切割的功率控制及其与运动的同步控制

王世勇 李迪 陈超   

  1. 华南理工大学 机械与汽车工程学院, 广东 广州 510640
  • 收稿日期:2009-05-07 修回日期:2009-09-18 出版日期:2010-03-25 发布日期:2010-03-25
  • 通信作者: 王世勇(1981-),男,博士生,主要从事高性能嵌入式控制系统研究. E-mail:drowsy105@163.com
  • 作者简介:王世勇(1981-),男,博士生,主要从事高性能嵌入式控制系统研究.
  • 基金资助:

    国家自然科学基金资助项目(50875090,50575075);广东省教育部产学研结合项目(2007A090302011)

Application of Intelligent Agent-Based Simulation to Electricity Market

Wang Shi-Yong   Li Di  Chen Chao   

  1. School of Mechanical and Automotive Engineering,South China University of Technology,Guangzhou 510640,Guangdong,China
  • Received:2009-05-07 Revised:2009-09-18 Online:2010-03-25 Published:2010-03-25
  • Contact: 王世勇(1981-),男,博士生,主要从事高性能嵌入式控制系统研究. E-mail:drowsy105@163.com
  • About author:王世勇(1981-),男,博士生,主要从事高性能嵌入式控制系统研究.
  • Supported by:

    国家自然科学基金资助项目(50875090,50575075);广东省教育部产学研结合项目(2007A090302011)

摘要: 为了使得激光功率跟随加工速度按照所需比率同步变化,建立了材料单位面积激光功率与切缝深度、激光器控制信号与输出功率间的函数关系.结合闭环控制策略,实现了任意材料、任意有效切缝深度下的激光功率控制以及激光功率与运动的精确同步控制.实验结果表明:该控制算法能够保证一致的切缝深度,获得均匀一致的切割质量;其功率控制精度约为1.47%,切缝深度误差在1%以内

关键词: 激光切割, 功率控制, 同步控制

Abstract:

The intelligent agent-based simulation has become a novel and powerful tool in the study of electricity market. This paper deals with the construction of a simulation system of electricity market. In the investigation, first, the intelligent-agent learning algorithms suitable for simulating the strategic bidding of electricity firms are in- troduced. Next, the applications of the VRE-learning algorithm, the Q-learning algorithm and the greedy algorithm to the simulation system are illustrated, and the corresponding implementation frameworks are proposed. Then, the techniques for the learning algotrithms to handle the convergence of agent bidding are discussed. Finally, an exam- ple is performed to test the effectiveness of the intelligent agent-based simulation. The results indicate that the intel- ligent-agent learning algorithms are capable of simulating the rational bidding behavior of electricity firms.

Key words: electricity market, intelligent agent, simulation, VRE-learning algorithm, Q-learning algorithm, greedy algorithm