期刊
IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS
卷 30, 期 6, 页码 1780-1789出版社
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/TNNLS.2018.2873676
关键词
Finite-time algorithm; distributed average tracking (DAT); adaptive-gain algorithm; multiple signals; non-linear dynamics
类别
资金
- National Nature Science Foundation of China [61603301, 61603300, 61722303, 61673104]
- China Postdoctoral Science Foundation [2017M623244, 2018T111097, 2018M633575]
- Natural Science Foundation of Shanxi Province of China [2017JQ6016, 2018JQ6073]
- Fundamental Research for Science and Technology Planning Project of Shenzhen [JCYJ20170306153912850]
- Source Innovation Program of Qindao [18-2-2-39-jch]
- Natural Science Foundation of Jiangsu Province of China [BK20170079]
- National Priority Research Project through Qatar National Research Fund [NPRP 9 166-1031]
- Australian Research Council [DE180101268]
- Fundamental Research Funds for the Central Universities of China [3102018jcc038, 2242018k1G004]
This paper studies the distributed average tracking (DAT) problem for multiple reference signals described by the second-order nonlinear dynamical systems. Leveraging the state-dependent gain design and the adaptive control approaches, a couple of DAT algorithms are developed in this paper, which are named finite-time and adaptive-gain DAT algorithms. Based on the finite-time one, the states of the physical agents in this paper can track the average of the time-varying reference signals within a finite settling time. Furthermore, the finite settling time is also estimated by considering a well-designed Lyapunov function in this paper. Compared with asymptotical DAT algorithms, the proposed finite-time algorithm not only solve finite-time DAT problems but also ensure states of physical agents to achieve an accurate average of the multiple signals. Then, an adaptive-gain DAT algorithm is designed. Based on the adaptive-gain one, the DAT problem is solved without global information. Thus, it is fully distributed. Finally, numerical simulations show the effectiveness of the theoretical results.
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