Initial states iterative learning for three-dimensional ballistic endpoint control

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• 作者：Jiaolong Liu ; Xinmin Dong ; Jianping Xue ; Zutong Wang ; Zongcheng Liu
• 关键词：Ballistic control ; Iterative learning control ; Space ; learning control ; Initial state learning ; Final state control ; Projectile motion
• 刊名：Memetic Computing
• 出版年：2017
• 出版时间：March 2017
• 年：2017
• 卷：9
• 期：1
• 页码：31-41
• 全文大小：
• 刊物类别：Engineering
• 刊物主题：Appl.Mathematics/Computational Methods of Engineering; Artificial Intelligence (incl. Robotics); Complex Systems; Control, Robotics, Mechatronics; Bioinformatics; Applications of Mathematics;
• 出版者：Springer Berlin Heidelberg
• ISSN：1865-9292
• 卷排序：9

文摘

In this paper, an initial states iterative learning control algorithm is proposed for control of the ballistic endpoint displacement in three-dimensional space, where the target is moving and the projectile experiences system uncertainties. The characteristics of the three-dimensional ballistic process are formulated and explored, and the learning algorithm is proposed in the spatial domain. The algorithm consists of two parts. First, the initial speed and angles are iteratively learned to make the projectile attain a fixed position. Second, the shooting time is learned to tune the arrival time of the projectile. Since the dimensions of the solution space are larger than that of the task space, three control manners, including shooting speed, shooting angle and their combination, are researched respectively. Through rigorously analyzed, it is proved that the algorithm is convergent and the multiple initial states can be adjusted simultaneously. Finally, an example of practical cannonball projection is presented to verify the effectiveness of the proposed algorithms.