An RNN-Based Performance Identification Model for Multi-Agent Containment Control Systems

Wei Liu, Fei Teng (), Xiaotian Fang (), Yuan Liang and Shiliang Zhang
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Wei Liu: School of Navigation, Dalian Maritime University, Dalian 116026, China
Fei Teng: College of Marine Electrical Engineering, Dalian Maritime University, Dalian 116026, China
Xiaotian Fang: Research Institute of Intelligent Networks, Zhejiang Lab, Hangzhou 311121, China
Yuan Liang: Research Institute of Intelligent Networks, Zhejiang Lab, Hangzhou 311121, China
Shiliang Zhang: Department of Informatics, University of Oslo, 0313 Oslo, Norway

Mathematics, 2023, vol. 11, issue 12, 1-16

Abstract: In the containment control problem of multi-agent systems (MASs), the convergence of followers is always a potential threat to the security of system operations. From the perspective of system topology, the inherently non-linear properties of the algebraic connectivity of the follower2follower (F2F) network, combined with the influence of the leader2follower (L2F) topology on the system, make it difficult to design the convergence positions of the followers through mere mathematical analysis. Therefore, in the background of temporary networking tasks for large-scale systems, to achieve the goal of forecasting the performance of the whole system when networking is only completed with local information, this paper investigates the application and effectiveness of recurrent neural networks (RNNs) in the containment control system performance identification, thus improving the efficiency of system networking while ensuring system security. Two types of identification models based on two types of neural networks (NNs), MLP and standard RNN are developed, according to the range of information required for performance identification. Evaluation of the models is carried out by means of the coefficient of determination ( R 2 ) as well as the root-mean-square error (RMSE). The results show that each model may produce a better forecasting accuracy than the other models in specific cases, with models based on the standard RNN possessing smaller errors. With the proposed method, model identification can be achieved, but in-depth development of the model in further studies is still necessary to the extent the accuracy of the model.

Keywords: RNN; neural network; MAS; containment control; topology; polymorphic network (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2023
References: View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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