控制理论(社会学)
弹道
计算机科学
理论(学习稳定性)
方案(数学)
跟踪误差
自适应控制
集合(抽象数据类型)
功能(生物学)
控制(管理)
人工神经网络
跟踪(教育)
数学优化
数学
人工智能
机器学习
数学分析
物理
天文
进化生物学
生物
程序设计语言
心理学
教育学
作者
Zhuwu Shao,Yujuan Wang,Xiang Chen
出处
期刊:IEEE transactions on neural networks and learning systems
[Institute of Electrical and Electronics Engineers]
日期:2022-01-01
卷期号:: 1-10
被引量:1
标识
DOI:10.1109/tnnls.2022.3189951
摘要
In this work, an online solution for reconstructing and predicting the uncertain target trajectory in real-time is proposed based on general regression neural network (GRNN). On this basis, an adaptive tracking control scheme guaranteeing prescribed performance is suggested for a class of strict-feedback systems with unknown control directions. In contrast to existing trajectory reconstruction methods, the one presented in this note does not require prior modeling of the uncertain target or offline training. Contrary to most current state-of-the-art prescribed performance control (PPC) technology, a novel time-varying scaling function and its corresponding translation function are introduced such that no strict constraints on initial conditions are needed, that is, global stability is achieved. The proposed control scheme allows the output of the system to chase the predicted value of the uncertain target, and the tracking error converges to a prescribed small set within a preassigned time, despite unmatched uncertainties and unknown control directions. The benefits of the proposed control scheme are confirmed by numerical simulations.
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