.png){kind=icon}
返回
Point-to-Point Iterative Learning Control With Optimal Tracking Time Allocation
.png){kind=icon}
OA
AI
DOI：10.1109/TCST.2017.2735358.png)
.png){kind=icon}
.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
Iterative learning control (ILC) is a high-performance tracking control design method for systems operating in a repetitive manner. This paper proposes a novel design methodology that extends the recently developed point-to-point ILC framework to allow automatic via-point time allocation within a given point-to-point tracking task, leading to significant performance improvements, e.g., energy reduction. The problem is formulated into an optimization framework with via-point temporal constraints and a reference tracking requirement, for which a two-stage design approach is developed. This yields an algorithmic solution, which minimizes input energy based on norm optimal ILC and gradient minimization. The algorithm is further expanded to incorporate system constraints into the design, prior to experimental validation on a gantry robot test platform to confirm its feasibility in practical applications.
Keyword:
Constraint handling
point-to-point iterative learning control (ILC)
AI总结
对已上传原文的论文进行重点信息的提取，主要内容包括：简要概述、研究摘要、背景介绍、关键亮点、图文解析、展望与总结。
期刊
IF:
3.9
论文数:
4.9K
被引数:
1.7W
机构
引用论文
Excellent product selectivity towards 2-phenyl-acetaldehyde and styrene oxide using manganese oxide and cobalt oxide NPs for the selective oxidation of styrene使用氧化锰和氧化钴np选择性氧化苯乙烯，对2-苯基乙醛和氧化苯乙烯具有优异的产品选择性
PREAI
分享收藏A Data-Driven Constrained Norm-Optimal Iterative Learning Control Framework for LTI SystemsLTI系统的数据驱动约束范数最优迭代学习控制框架
OAAI
OAAI分享收藏Initial state iterative learning for final state control in motion systems运动系统最终状态控制的初始状态迭代学习.png)
AUTOMATICA
AUTOMATICAIF5.9
PREAI
分享收藏
查看更多内容