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- W4296667413 abstract "In this paper, an adaptive critic method based on neural networks is established to solve the tracking control problem for multi-person zero-sum games with constrained nonlinear dynamics. First, an augmented system is constructed with the tracking error system and the reference system, an appropriate function is introduced to handle the constrained problem, and a constrained tracking Hamilton–Jacobi-Isaacs (HJI) equation is derived for the augmented system. Then, a constrained tracking design with neural critic learning for multi-person zero-sum games is developed to approximately solve the tracking HJI equation with input constraints. A new updating rule is given and only one critic network is employed during neural critic learning. In addition, we prove that the tracking error in the augmented system is uniformly ultimately bounded by using Lyapunov’s direct method. Finally, an example is given to verify the effectiveness of the proposed method. In this example, we make the number of control inputs less than the number of disturbance inputs." @default.
- W4296667413 created "2022-09-23" @default.
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- W4296667413 date "2022-11-01" @default.
- W4296667413 modified "2023-10-18" @default.
- W4296667413 title "Neural critic learning for tracking control design of constrained nonlinear multi-person zero-sum games" @default.
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- W4296667413 doi "https://doi.org/10.1016/j.neucom.2022.09.103" @default.
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