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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: |
Improving the Critic Learning for Event-Based Nonlinear H-infinity Control Design
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| 英文论文题目: |
Improving the Critic Learning for Event-Based Nonlinear H-infinity Control Design
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| 论文题目英文: |
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| 作者: |
Wang, Ding
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| 论文出处: |
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| 刊物名称: |
IEEE TRANSACTIONS ON CYBERNETICS
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| 年: |
2017
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| 卷: |
47 |
| 期: |
10 |
| 页: |
3417-3428 |
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| 收录类别: |
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| 影响因子: |
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| 摘要: |
In this paper, we aim at improving the critic learning criterion to cope with the event-based nonlinear H-infinity state feedback control design. First of all, the H-infinity control problem is regarded as a two-player zero-sum game and the adaptive critic mechanism is used to achieve the minimax optimization under event-based environment. Then, based on an improved updating rule, the event-based optimal control law and the time-based worst-case disturbance law are obtained approximately by training a single critic neural network. The initial stabilizing control is no longer required during the implementation process of the new algorithm. Next, the closed-loop system is formulated as an impulsive model and its stability issue is handled by incorporating the improved learning criterion. The infamous Zeno behavior of the present event-based design is also avoided through theoretical analysis on the lower bound of the minimal intersample time. Finally, the applications to an aircraft dynamics and a robot arm plant are carried out to verify the efficient performance of the present novel design method. |
| 英文摘要: |
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