| 引用本文: | 潘天红,李少远.基于即时学习的非线性系统自适应PID控制[J].控制理论与应用,2009,26(10):1180~1184.[点击复制] |
| PAN Tian-hong,LI Shao-yuan.Adaptive PID control for nonlinear systems based on lazy learning[J].Control Theory and Technology,2009,26(10):1180~1184.[点击复制] |
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| 基于即时学习的非线性系统自适应PID控制 |
| Adaptive PID control for nonlinear systems based on lazy learning |
| 摘要点击 2539 全文点击 1955 投稿时间：2008-02-25 修订日期：2009-08-09 |
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| DOI编号 10.7641/j.issn.1000-8152.2009.10.CCTA080128 |
| 2009,26(10):1180-1184 |
| 中文关键词 广义最小方差 即时学习 k矢量近邻 PID控制器 |
| 英文关键词 general minimum variance lazy learning k-vector nearest neighbors PID controller |
| 基金项目 国家自然科学基金资助项目(60474051, 60904053); 江苏大学高级专业人才科研启动基金资助项目(08JDG046). |
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| 中文摘要 |
| 当使用先进策略整定PID控制器参数时, 往往要依赖于系统所辨识的模型, 而模型的精度与优化算法的计算效率直接影响到系统的控制效果. 本文利用即时学习算法的本质自适应特点(建模数据在时间与空间上相邻性),来提高辨识模型的精度，并基于广义最小方差的性能指标, 用等价多项式的方法, 推导出PID形式的控制律, 从而避免其他优化算法带来的计算量, 提高了控制精度与计算效率. 仿真结果验证了该方法的有效性. |
| 英文摘要 |
| When applying advanced strategies to determine the parameters of a PID controller, a model need to be identified for the controlled system. The predicted precision and the computation efficiency of the identifying algorithm
directly affect the control performance of the system. To improve the precision, lazy learning algorithm, which has a essentially adaptive characteristics(i.e.,in which the data used for modeling are not only neighbors in time domain, but also neighbors in space domain), is used to identify the model of system. By employing the generalized minimum variance as
the performance function and using the polynomial method, the control-law is derived for the PID controller, in which its parameters are tuned online in the process of the lazy learning identification. Simulation results show good performances of this algorithm. |
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