Robust Monotonic Convergent Iterative Learning Control
Robust Monotonic Convergent Iterative Learning Control
复制标题
DOI：
10.1109/tac.2015.2457785
复制
发表时间：
2016-04
影响因子：
6.8
通讯作者：
Tong Duy Son;G. Pipeleers;J. Swevers
中科院分区：
文献类型：
--
作者：
Tong Duy Son;G. Pipeleers;J. Swevers
本技术说明提出了一种处理迭代学习控制 (ILC) 中模型不确定性的方法。模型不确定性通常会降低传统学习算法的性能。为了解决这个问题，引入了稳健的最坏情况范数最优 ILC 设计。设计问题被重新表述为凸优化问题，可以有效地解决。技术说明还表明，所提出的鲁棒 ILC 相当于具有试验变化参数的传统规范最优 ILC；因此，分析了鲁棒性和收敛速度之间的设计权衡。
This technical note presents an approach to deal with model uncertainty in iterative learning control (ILC). Model uncertainty generally degrades the performance of conventional learning algorithms. To deal with this problem, a robust worst-case norm-optimal ILC design is introduced. The design problem is reformulated as a convex optimization problem, which can be solved efficiently. The technical note also shows that the proposed robust ILC is equivalent to conventional norm-optimal ILC with trial-varying parameters; accordingly, the design tradeoff between robustness and convergence speed is analyzed.