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| 论文编号: |
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| 第一作者所在部门: |
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| 中文论文题目: |
Chaotic system optimal tracking using data-based synchronous method with unknown dynamics and disturbances
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| 英文论文题目: |
Chaotic system optimal tracking using data-based synchronous method with unknown dynamics and disturbances
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| 论文题目英文: |
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| 作者: |
Song, Ruizhuo
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| 论文出处: |
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| 刊物名称: |
CHINESE PHYSICS B
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| 年: |
2017
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| 卷: |
26 |
| 期: |
3 |
| 页: |
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| 联系作者: |
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| 收录类别: |
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| 影响因子: |
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| 摘要: |
We develop an optimal tracking control method for chaotic system with unknown dynamics and disturbances. The method allows the optimal cost function and the corresponding tracking control to update synchronously. According to the tracking error and the reference dynamics, the augmented system is constructed. Then the optimal tracking control problem is defined. The policy iteration (PI) is introduced to solve the min-max optimization problem. The off-policy adaptive dynamic programming (ADP) algorithm is then proposed to find the solution of the tracking Hamilton-Jacobi-Isaacs (HJI) equation online only using measured data and without any knowledge about the system dynamics. Critic neural network (CNN), action neural network (ANN), and disturbance neural network (DNN) are used to approximate the cost function, control, and disturbance. The weights of these networks compose the augmented weight matrix, and the uniformly ultimately bounded (UUB) of which is proven. The convergence of the tracking error system is also proven. Two examples are given to show the effectiveness of the proposed synchronous solution method for the chaotic system tracking problem. |
| 英文摘要: |
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