反推
控制理论(社会学)
趋同(经济学)
李雅普诺夫函数
振动
人工神经网络
计算机科学
约束(计算机辅助设计)
收敛速度
振动控制
瞬态(计算机编程)
符号函数
自适应控制
数学优化
数学
控制(管理)
钥匙(锁)
非线性系统
人工智能
经济增长
量子力学
计算机安全
操作系统
物理
数学分析
经济
几何学
作者
Wei He,Fengshou Kang,Linghuan Kong,Yanghe Feng,Guangquan Cheng,Changyin Sun
出处
期刊:IEEE transactions on cybernetics
[Institute of Electrical and Electronics Engineers]
日期:2022-07-01
卷期号:52 (7): 5973-5983
被引量:38
标识
DOI:10.1109/tcyb.2021.3064865
摘要
With the more extensive application of flexible robots, the expectation for flexible manipulators is also increasing rapidly. However, the fast convergence will cause the increase of vibration amplitude to some extent, and it is difficult to obtain vibration suppression and satisfactory transient performance at the same time. In order to deal with the problem, a fixed-time learning control method is proposed to realize the fast convergence. The constraint on system outputs, system uncertainty, and input saturation is addressed under the fixed-time convergence framework. A novel adaptive law for neural networks is integrated into the backstepping method, which enhances the learning rate of neural networks. The imposed constraint on the vibration amplitude is guaranteed by using the barrier Lyapunov function (BLF). Moreover, the chattering problem is addressed by approximating the sign function smoothly. In the end, some simulations have been carried out to show the effectiveness of the proposed method.
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