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Neural-network-based integral sliding-mode tracking control of second-order multi-agent systems with unmatched disturbances and completely unknown dynamics

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  • Intelligent Control and Applications
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Abstract

This paper investigates the tracking control problem of second-order multi-agent systems (MASs) in the presence of unmatched disturbances and completely unknown dynamics. The extended state observer (ESO) and neural networks (NNs) are utilized to estimate and compensated the unmatched disturbances and unknown dynamics, respectively. By constructed a novel integral sliding-mode manifold incorporated with ESO output, a neural-network-based control algorithm is developed. Meanwhile, by Lyapunov theoretical analysis, the UUB stability of the tracking errors as well as within a sufficiently small region is guaranteed by the appropriate choice of the parameters. Simulation results show that the proposed method exhibits much better control performances than the traditional I-SMC method, such as great robustness, reduced chattering and more accurate.

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Author information

Authors and Affiliations

  1. Department of Aerospace Engineering, High-Tech Institute of Xi’an, Xi’an, 710025, China

    Xi Ma & Bing He

  2. Department of Computer Science and Technology, Tsinghua University, State Key Lab. of Intelligent Technology and Systems, Tsinghua National Laboratory for Information Science and Technology, Beijing, 100084, China

    Xi Ma, Fuchun Sun & HongBo Li

Authors
  1. Xi Ma
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  2. Fuchun Sun
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  3. HongBo Li
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  4. Bing He
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Corresponding author

Correspondence to Xi Ma.

Additional information

Recommended by Associate Editor Sung Jin Yoo under the direction of Editor Euntai Kim. This work was supported by the Natural Science Foundation of P. R. China under grants 61403399.

Xi Ma received his B.S. and M.S. degrees from Xi’an High-tech Institution, Xi’an, China, in 2010 and 2013, respectively. He is currently working towards a Ph.D. degree at the Department of Computer Science and Technology, Tsinghua University, Beijing, China. His research interests include cooperative control of multi-agent system, robust control, nonlinear control.

Fuchun Sun received his B.S. and M.S. degrees from Naval Aeronautical Engineering Academy, Yantai, China, in 1986 and 1989, respectively, and his Ph.D. degree from Tsinghua University, Beijing, China, in 1998. He was with Department of Automatic Control, Naval Aeronautical Engineering Academy. From 1998 to 2000, he was Postdoctoral Fellow with Department of Automation, Tsinghua University. He is currently a Professor with Department of Computer Science and Technology, Tsinghua University. His research interests include intelligent control, neural networks, fuzzy systems, variable structure control, nonlinear systems, and robotics. Dr. Sun is the recipient of the excellent Doctoral Dissertation Prize of China in 2000 and the Choon-Gang Academic Award by Korea in 2003, and was recognized as Distinguished Young Scholar in 2006 by the Natural Science Foundation of China.

Hongbo Li received his Ph.D. degree from Tsinghua University, China, in 2009. He is currently an assistant professor of the Department of Computer Science and Technology at Tsinghua University. His research interests include networked control systems and intelligent control.

Bing He received his B.S., M.S., and Ph.D. degrees from Xi’an High-tech Institution, Xi’an, China, in 2005, 2008, and 2012. He is currently an assistant professor of Xi’an High-tech Institution. His research interests include nonlinear control, flight vehicle design.

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Ma, X., Sun, F., Li, H. et al. Neural-network-based integral sliding-mode tracking control of second-order multi-agent systems with unmatched disturbances and completely unknown dynamics. Int. J. Control Autom. Syst. 15, 1925–1935 (2017). https://doi.org/10.1007/s12555-016-0057-z

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  • DOI: https://doi.org/10.1007/s12555-016-0057-z

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