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Robust adaptive self-organizing neuro-fuzzy tracking control of UUV with system uncertainties and unknown dead-zone nonlinearity

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Abstract

In this paper, a robust adaptive self-organizing neuro-fuzzy control (RASNFC) scheme for tracking of unmanned underwater vehicle with uncertainties and the unknown dead-zone nonlinearity is proposed. The proposed RASNFC scheme comprises an estimation-based adaptive controller (EBAC) using a self-organizing neuro-fuzzy network (SNFN) and a robust controller. The EBAC controller is constructed with a novel sliding mode reaching law control framework, and the unknown dynamic function is identified by the SNFN approximator which is able to online self-construct a neuro-fuzzy network with dynamic structure by generating and pruning fuzzy rule. The robust controller is employed to provide the finite \(L_{2}\)-gain property to cope with reconstruction errors such that the robustness of the entire closed-loop control system is enhanced. Theoretical analysis shows that tracking errors and their derivatives are asymptotically stable and all signals in the closed-loop system are bounded. Comparative simulation results demonstrate the effectiveness and superiority of the proposed RASNFC scheme.

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Acknowledgements

The authors would like to thank Associate Editor and the referees for their very careful comments and helpful suggestions which improved this technical note significantly. This work is supported by the National Natural Science Foundation of PR China (under Grants 51479018 and 51379002) and Fundamental Research Funds for the Central Universities of PR China (under Grants 3132016335 and 3132016314).

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  1. Marine Engineering College, Dalian Maritime University, Dalian, 116026, China

    Siyuan Liu, Yancheng Liu & Ning Wang

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  1. Siyuan Liu
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  2. Yancheng Liu
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  3. Ning Wang
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Correspondence to Yancheng Liu or Ning Wang.

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Liu, S., Liu, Y. & Wang, N. Robust adaptive self-organizing neuro-fuzzy tracking control of UUV with system uncertainties and unknown dead-zone nonlinearity. Nonlinear Dyn 89, 1397–1414 (2017). https://doi.org/10.1007/s11071-017-3524-z

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