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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: |
Error Bound Analysis of Q-Function for Discounted Optimal Control Problems With Policy Iteration
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| 英文论文题目: |
Error Bound Analysis of Q-Function for Discounted Optimal Control Problems With Policy Iteration
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| 论文题目英文: |
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| 作者: |
Yan, Pengfei
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| 论文出处: |
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| 刊物名称: |
IEEE TRANSACTIONS ON SYSTEMS MAN CYBERNETICS-SYSTEMS
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| 年: |
2017
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| 卷: |
47 |
| 期: |
7 |
| 页: |
1207-1216 |
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| 影响因子: |
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| 摘要: |
In this paper, we present error bound analysis of the Q-function for the action-dependent adaptive dynamic programming for solving discounted optimal control problems of unknown discrete-time nonlinear systems. The convergence of Q-functions derived by a policy iteration algorithm under ideal conditions is given. Considering the approximated errors of the Q-function and control policy in the policy evaluation step and policy improvement step, we establish error bounds of approximate Q-functions in each iteration. With the given boundedness conditions, the approximate Q-function will converge to a finite neighborhood of the optimal Q-function. To implement the presented algorithm, two three-layer neural networks are employed to approximate the Q-function and the control policy, respectively. Finally, a simulation example is utilized to verify the validity of the presented algorithm. |
| 英文摘要: |
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