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Adaptive Fuzzy Distributed Formation Tracking for Second-order Nonlinear Multi-agent Systems with Prescribed Performance

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Neural Computing for Advanced Applications (NCAA 2022)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 1637))

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Abstract

The paper investigates the distributed prescribed performance output formation tracking problem of second-order nonlinear multi-agent systems subject to uncertain disturbances. The formation is realized in a leader-follower structure, which means all followers can form a desired formation pattern while tracking the leader. For accomplishing the formation with prescribed performance, firstly, a time-varying barrier Lyapunov function(TV-BLF) consisting of formation error and performance function is introduced. Then, an adaptive formation protocol is proposed based on the TV-BLF considering both matched and mismatched disturbances. Besides, unknown nonlinear terms in the dynamic models of agents are approximated by adaptive fuzzy logic systems. Further, it is proved rigorously that under the proposed method, the formation errors can satisfy the preset performance and converge to a predefined small region around the origin. At last, a simulation example is performed to validate the performance and the superiority of the developed scheme.

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Correspondence to Bo Wang .

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An, B., Zheng, Z., Wang, B., Fan, H., Liu, L., Wang, Y. (2022). Adaptive Fuzzy Distributed Formation Tracking for Second-order Nonlinear Multi-agent Systems with Prescribed Performance. In: Zhang, H., et al. Neural Computing for Advanced Applications. NCAA 2022. Communications in Computer and Information Science, vol 1637. Springer, Singapore. https://doi.org/10.1007/978-981-19-6142-7_12

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  • DOI: https://doi.org/10.1007/978-981-19-6142-7_12

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  • Publisher Name: Springer, Singapore

  • Print ISBN: 978-981-19-6141-0

  • Online ISBN: 978-981-19-6142-7

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