Distributed anti-windup NN-sliding mode formation control of multi-ships with minimum cost
ISA Transactions, ISSN: 0019-0578, Vol: 138, Page: 49-62
2023
- 7Citations
- 2Captures
- 1Mentions
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Dalian Maritime University Reports Findings in Networks (Distributed anti-windup NN-sliding mode formation control of multi-ships with minimum cost)
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Article Description
Due to the harsh marine environment, the communication cost of multi-ship formation is expensive, but it is often ignored in the existing research. On this basis, this paper proposes a novel distributed anti-windup neural network (NN)-sliding mode formation controller of multi-ships with minimum cost. Firstly, distributed control is applied to devise the formation controller of multi-ships because it is a promising solution for the problem of single point failure. Secondly, the Dijkstra algorithm is introduced to optimize the communication topology, and then an optimized communication topology with minimum cost is used in the distributed formation controller design. Thirdly, to alleviate the influence of input saturation, an anti-windup mechanism is devised by combining an auxiliary design system with sliding mode control and radial basis function neural network method; and then a novel distributed anti-windup neural network-sliding mode formation controller of multi-ships is obtained, which can also handle the problem of nonlinearity, model uncertainty, and time-varying disturbances of ship motion. On the strength of Lyapunov theory, the closed-loop signals are proved to be stable. Multiple comparative simulations are carried out to validate the effectiveness and advantage of the proposed distributed formation controller.
Bibliographic Details
http://www.sciencedirect.com/science/article/pii/S0019057823001192; http://dx.doi.org/10.1016/j.isatra.2023.03.011; http://www.scopus.com/inward/record.url?partnerID=HzOxMe3b&scp=85150784020&origin=inward; http://www.ncbi.nlm.nih.gov/pubmed/36973152; https://linkinghub.elsevier.com/retrieve/pii/S0019057823001192; https://dx.doi.org/10.1016/j.isatra.2023.03.011
Elsevier BV
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