期刊
IEEE TRANSACTIONS ON CYBERNETICS
卷 52, 期 7, 页码 7039-7048出版社
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/TCYB.2020.3044883
关键词
Convergence; Artificial neural networks; Vehicle dynamics; Compounds; Radar tracking; Asymptotic stability; Roads; Adaptive neural networks (NNs) control; compound tracking control; distributed cooperative control; platoon stability; vehicle platoon systems
类别
资金
- National Natural Science Foundation of China [62033003, 62003097, 62003098]
- Local Innovative and Research Teams Project of Guangdong Special Support Program [2019BT02X353]
- China Postdoctoral Science Foundation [2019M662812, 2019M662813, 2020T130124]
- Joint Funds of Guangdong Basic and Applied Basic Research Foundation [2019A1515110505]
This article introduces a definition of compound tracking control and a finite-time performance function, and utilizes methods such as adaptive neural networks to design a distributed cooperative regulation protocol. Simulation experiments confirm the effectiveness of the theoretical findings.
This article focuses on the distributed cooperative compound tracking issue of the vehicular platoon. First, a definition, called compound tracking control, is proposed, which means that the practical finite-time stability and asymptotical convergence can be simultaneously satisfied. Then, a modified performance function, named finite-time performance function, is designed, which possesses the faster convergence rate compared to the existing ones. Moreover, the adaptive neural network (NN), prescribed performance technique, and backstepping method are utilized to design a distributed cooperative regulation protocol. It is worth noting that the convergence time of the proposed algorithm does not depend on the initial values and design parameters. Finally, simulation experiments are given to further verify the effectiveness of the presented theoretical findings.
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