Distributed zero-sum differential game for multi-agent systems in strict-feedback form with input saturation and output constraint

• Published in: Neural networks Vol. 106; pp. 8 - 19
• Main Authors: Sun, Jingliang, Liu, Chunsheng
• Format: Journal Article
• Language: English
• Published: United States Elsevier Ltd 01.10.2018
• Subjects: Adaptive dynamic programming (ADP); Algorithms; Command filtered backstepping; Computer Simulation; Distributed differential game; Feedback; Input saturation; Neural Networks (Computer); Nonlinear Dynamics; Output constraint; Uncertainty; Command filtered backstepping; Input saturation; Output constraint; Adaptive dynamic programming (ADP); Distributed differential game
• ISSN: 0893-6080; 1879-2782; 1879-2782
• DOI: 10.1016/j.neunet.2018.06.007

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.