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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: |
Adaptive dynamic programming for robust neural control of unknown continuous-time non-linear systems
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| 英文论文题目: |
Adaptive dynamic programming for robust neural control of unknown continuous-time non-linear systems
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| 论文题目英文: |
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| 作者: |
Yang, Xiong
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| 论文出处: |
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| 刊物名称: |
IET CONTROL THEORY AND APPLICATIONS
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| 年: |
2017
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| 卷: |
11 |
| 期: |
14 |
| 页: |
2307-2316 |
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| 收录类别: |
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| 影响因子: |
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| 摘要: |
The design of robust controllers for continuous-time (CT) non-linear systems with completely unknown non-linearities is a challenging task. The inability to accurately identify the non-linearities online or offline motivates the design of robust controllers using adaptive dynamic programming (ADP). In this study, an ADP-based robust neural control scheme is developed for a class of unknown CT non-linear systems. To begin with, the robust non-linear control problem is converted into a non-linear optimal control problem via constructing a value function for the nominal system. Then an ADP algorithm is developed to solve the non-linear optimal control problem. The ADP algorithm employs actor-critic dual networks to approximate the control policy and the value function, respectively. Based on this architecture, only system data is necessary to update simultaneously the actor neural network (NN) weights and the critic NN weights. Meanwhile, the persistence of excitation assumption is no longer required by using the Monte Carlo integration method. The closed-loop system with unknown non-linearities is demonstrated to be asymptotically stable under the obtained optimal control. Finally, two examples are provided to validate the developed method. |
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