控制理论与应用2009，Vol.26Issue(10)：1180-1184,5.
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
Abstract
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.关键词
广义最小方差/即时学习/κ矢量近邻/PID控制器Key words
general minimum variance/ lazy learning/ k-vector nearest neighbors/ PID controller分类
信息技术与安全科学引用本文复制引用
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):1180-1184,5.基金项目
国家自然科学基金资助项目(60474051,60904053) （60474051,60904053）
江苏大学高级专业人才科研启动基金资助项目(08JDG046). （08JDG046）
