迭代学习控制
计算机科学
模型预测控制
终端(电信)
构造(python库)
集合(抽象数据类型)
国家(计算机科学)
迭代法
控制(管理)
功能(生物学)
人工智能
控制理论(社会学)
控制器(灌溉)
算法
电信
进化生物学
农学
生物
程序设计语言
作者
Ugo Rosolia,Francesco Borrelli
出处
期刊:IEEE Transactions on Automatic Control
[Institute of Electrical and Electronics Engineers]
日期:2018-07-01
卷期号:63 (7): 1883-1896
被引量:275
标识
DOI:10.1109/tac.2017.2753460
摘要
A learning model predictive controller for iterative tasks is presented. The controller is reference-free and is able to improve its performance by learning from previous iterations. A safe set and a terminal cost function are used in order to guarantee recursive feasibility and nondecreasing performance at each iteration. This paper presents the control design approach, and shows how to recursively construct terminal set and terminal cost from state and input trajectories of previous iterations. Simulation results show the effectiveness of the proposed control logic.
科研通智能强力驱动
Strongly Powered by AbleSci AI