摘要
针对一类非线性非仿射离散时间系统,提出了高阶无模型自适应迭代学习控制方案.控制器的设计和分析仅依赖于系统的输入/输出(I/O)数据,不需要已知任何其他知识.该方法采用了高阶学习律,可利用更多以前重复过程中的控制信息提高系统收敛性,且学习增益可通过"拟伪偏导数"更新律迭代调节.仿真结果验证了所提出算法的有效性.
A higher-order model-free adaptive iterative learning control is presented for a class of nonlinear and nonaffine discrete-time systems. The control design and analysis depend on the I/0 data of the system without reqiring any other knowledge. By introducing higher-order learning law, this method can incorporate more control information of prievious tries to improve the convergence performance. Furthermore, the learning gain can be tuned iteratively by the ＂mimic pseudo partial derivative＂ updating law. Simulation results illustrate the validity of the presented method.
出处
《控制与决策》
EI
CSCD
北大核心
2008年第7期795-798,共4页
Control and Decision
基金
国家自然科学基金项目(60474038)
青岛科技大学启动基金项目(0022324)
关键词
无模型自适应控制
迭代学习控制
高阶学习控制律
学习增益
Model-free adaptive control
Iterative learning control
Higher-order learning control law
Learning gain
