Distributed zero-sum differential game for multi-agent systems in strict-feedback form with input saturation and output constraint
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所属单位：自动化学院
发表刊物：NEURAL NETWORKS
关键字：Distributed differential game Command filtered backstepping Output constraint Input saturation Adaptive dynamic programming (ADP)
摘要：This paper investigates the distributed differential game tracking problem for nonlinear multi-agent systems with output constraint under a fixed directed graph. Each follower can be taken as strict-feedback structure with uncertain nonlinearities and input saturation. Firstly, by utilizing the command filtered backstepping technique, the distributed tracking control problem of multi-agent systems in strict-feedback form can be transformed into an equivalent distributed differential game problem of tracking error dynamics in affine form by designing a distributed feedforward tracking controller, in which neural networks (NNs) and the auxiliary system are introduced to deal with the unknown nonlinearities and input saturation, respectively. Especially, a novel barrier Lyapunov function (BLF) is firstly introduced to tackle with the output constraint. Subsequently, by using adaptive dynamic programming (ADP) technique, the distributed zero-sum differential game strategy is derived, in which a critic network is constructed to approximate the cooperative cost function online with a novel updating law. Therefore, the whole distributed control scheme not only guarantees the closed-loop signals to be cooperatively uniformly ultimately bounded (CUUB), but also ensures the cooperative cost function to be minimized. Meanwhile, the output constraint and input saturation are not violated. Finally, simulation results demonstrate the effectiveness of the proposed method. (C) 2018 Elsevier Ltd. All rights reserved.
ISSN号：0893-6080
是否译文：否
发表时间：2018-10-01
合写作者：Sun, Jingliang
通讯作者：刘春生