Adaptive dynamic programming-based hierarchical decision-making of non-affine systems
In this paper, the problem of multiplayer hierarchical decision-making problem for non-affine systems is solved by adaptive dynamic programming. Firstly, the control dynamics are obtained according to the theory of dynamic feedback and combined with the original system dynamics to construct the affine augmented system. Thus, the non-affine multiplayer system is transformed into a general affine form. Then, the hierarchical decision problem is modeled as a Stackelberg game. In the Stackelberg game, the leader makes a decision based on the information of all followers, whereas the followers do not know each other's information and only obtain their optimal control strategy based on the leader's decision. Then, the augmented system is reconstructed by a neural network (NN) using input–output data. Moreover, a single critic NN is used to approximate the value function to obtain the optimal control strategy for each player. An extra term added to the weight update law makes the initial admissible control law no longer needed. According to the Lyapunov theory, the state of the system and the error of the weights of the NN are both uniformly ultimately bounded. Finally, the feasibility and validity of the algorithm are confirmed by simulation.
National Key Research and Development Program of China[2018AAA0100203];Basic and Applied Basic Research Foundation of Guangdong Province[2021A1515110870];National Natural Science Foundation of China;National Natural Science Foundation of China;
|WOS Research Area|
Computer Science ; Neurosciences & Neurology
Computer Science, Artificial Intelligence ; Neurosciences
|WOS Accession No|
|ESI Research Field|
Cited Times [WOS]:0
|Document Type||Journal Article|
|Department||School of System Design and Intelligent Manufacturing|
1.School of Automation,Guangdong University of Technology,Guangzhou,510006,China
2.School of Information and Communication Engineering,Hainan University,Haikou,570100,China
3.School of System Design and Intelligent Manufacturing,Southern University of Science and Technology,Shenzhen,518055,China
4.Department of Electrical and Computer Engineering,University of illinois Chicago,Chicago,60607,United States
|Corresponding Author Affilication||School of System Design and Intelligent Manufacturing|
|First Author's First Affilication||School of System Design and Intelligent Manufacturing|
Lin，Danyu,Xue，Shan,Liu，Derong,et al. Adaptive dynamic programming-based hierarchical decision-making of non-affine systems[J]. Neural Networks,2023,167:331-341.
Lin，Danyu,Xue，Shan,Liu，Derong,Liang，Mingming,&Wang，Yonghua.(2023).Adaptive dynamic programming-based hierarchical decision-making of non-affine systems.Neural Networks,167,331-341.
Lin，Danyu,et al."Adaptive dynamic programming-based hierarchical decision-making of non-affine systems".Neural Networks 167(2023):331-341.
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