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Noncooperative Model Predictive Game With Markov Jump Graph
Y. Xu, Y. Yuan, Z. Wang, and X. L. Li, “Noncooperative model predictive game with Markov jump graph,” IEEE/CAA J. Autom. Sinica, vol. 10, no. 4, pp. 931–944, Apr. 2023. doi: 10.1109/JAS.2023.123129
Authors:Yang Xu  Yuan Yuan  Zhen Wang  Xuelong Li
Affiliation:1. School of Astronautics, Northwestern Polytechnical University, Xi’an 710072, China;2. School of Computer Science and Center for Optical Imagery Analysis and Learning, Northwestern Polytechnical University, and also with the School of Cybersecurity, Northwestern Polytechnical University, Xi’an 710072, China;3. School of Artificial Intelligence, Optics and Electronics (iOPEN), Northwestern Polytechnical University, and also with the Key Laboratory of Intelligent Interaction and Applications, Northwestern Polytechnical University, Ministry of Industry and Information Technology, Xi’an 710072, China
Abstract:In this paper, the distributed stochastic model predictive control (MPC) is proposed for the noncooperative game problem of the discrete-time multi-player systems (MPSs) with the undirected Markov jump graph. To reflect the reality, the state and input constraints have been considered along with the external disturbances. An iterative algorithm is designed such that model predictive noncooperative game could converge to the so-called ε-Nash equilibrium in a distributed manner. Sufficient conditions are established to guarantee the convergence of the proposed algorithm. In addition, a set of easy-to-check conditions are provided to ensure the mean-square uniform bounded stability of the underlying MPSs. Finally, a numerical example on a group of spacecrafts is studied to verify the effectiveness of the proposed method. 
Keywords:Markov jump graph   model predictive control (MPC)   multi-player systems (MPSs)   noncooperative game   ε-Nash equilibrium
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