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Volume 10 Issue 1
Jan.  2023

IEEE/CAA Journal of Automatica Sinica

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Article Contents
W. Q. Cao, J. Yan, X. Yang, X. Y. Luo, and  X. P. Guan,  “Communication-aware formation control of AUVs with model uncertainty and fading channel via integral reinforcement learning,” IEEE/CAA J. Autom. Sinica, vol. 10, no. 1, pp. 159–176, Jan. 2023. doi: 10.1109/JAS.2023.123021
Citation: W. Q. Cao, J. Yan, X. Yang, X. Y. Luo, and  X. P. Guan,  “Communication-aware formation control of AUVs with model uncertainty and fading channel via integral reinforcement learning,” IEEE/CAA J. Autom. Sinica, vol. 10, no. 1, pp. 159–176, Jan. 2023. doi: 10.1109/JAS.2023.123021

Communication-Aware Formation Control of AUVs With Model Uncertainty and Fading Channel via Integral Reinforcement Learning

doi: 10.1109/JAS.2023.123021
Funds:  This work was supported in part by the National Natural Science Foundation of China (62222314, 61973263, 61873345, 62033011), the Youth Talent Program of Hebei (BJ2020031), the Distinguished Young Foundation of Hebei Province (F2022203001), the Central Guidance Local Foundation of Hebei Province (226Z3201G), and the Three-Three-Three Foundation of Hebei Province (C20221019)
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  • Most formation approaches of autonomous underwater vehicles (AUVs) focus on the control techniques, ignoring the influence of underwater channel. This paper is concerned with a communication-aware formation issue for AUVs, subject to model uncertainty and fading channel. An integral reinforcement learning (IRL) based estimator is designed to calculate the probabilistic channel parameters, wherein the multivariate probabilistic collocation method with orthogonal fractional factorial design (M-PCM-OFFD) is employed to evaluate the uncertain channel measurements. With the estimated signal-to-noise ratio (SNR), we employ the IRL and M-PCM-OFFD to develop a saturated formation controller for AUVs, dealing with uncertain dynamics and current parameters. For the proposed formation approach, an integrated optimization solution is presented to make a balance between formation stability and communication efficiency. Main innovations lie in three aspects: 1) Construct an integrated communication and control optimization framework; 2) Design an IRL-based channel prediction estimator; 3) Develop an IRL-based formation controller with M-PCM-OFFD. Finally, simulation results show that the formation approach can avoid local optimum estimation, improve the channel efficiency, and relax the dependence of AUV model parameters.


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    • Channel prediction, formation control and model uncertainty are considered together
    • An IRL-based estimator with M-PCM-OFFD is designed to predict the SNR of AUVs in positions that they have not yet visited and can effectively avoid failing into local optimum
    • An IRL-based formation controller with input saturation of thrusters is developed to guarantee communication-aware formation control of AUVs
    • Meanwhile, the nominal values of model parameters are not required to be known by the proposed formation controller as compared with the others works


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