控制理论(社会学)
稳健性(进化)
计算机科学
滑模控制
鲁棒控制
自适应控制
控制工程
控制系统
工程类
控制(管理)
人工智能
非线性系统
物理
生物化学
化学
量子力学
电气工程
基因
作者
Yunsong Hu,Huaicheng Yan,Hao Zhang,Meng Wang,Lu Zeng
出处
期刊:IEEE transactions on cybernetics
[Institute of Electrical and Electronics Engineers]
日期:2023-04-01
卷期号:53 (4): 2636-2646
被引量:45
标识
DOI:10.1109/tcyb.2022.3164739
摘要
In this article, a robust adaptive fixed-time sliding-mode control method is proposed for robotic systems with parameter uncertainties and input saturation. First, a model-based fixed-time controller is designed under the premise that the system parameters are known. Moreover, the unknown dynamics of robotic systems and the boundary of compounded disturbance are synthesized into a compounded uncertainty. Then, the Gaussian radial basis function neural networks (NNs) are selected to approximate the compounded uncertainty. In addition, the nonsingular fast terminal sliding-mode (NFTSM) control is incorporated into the proposed fixed-time control framework to enhance the robustness and convergence speed of unknown robotic systems. Finally, a comparative simulation based on a rigid manipulator shows the superiority and efficacy of the designed methods.
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