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Hamiltonian-Driven Hybrid Adaptive Dynamic Programming
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DOI：10.1109/TSMC.2019.2962103.png)
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摘要
En 中文
This article presents a model-based hybrid adaptive dynamic programming (ADP) framework consisting of continuous feedback-based policy evaluation and policy improvement steps as well as an intermittent policy implementation procedure. This results in an intermittent ADP with a quantifiable performance and guaranteed closed-loop stability of the equilibrium point. To investigate the effect of aperiodic sampling on the communication bandwidth and the control performance of the intermittent ADP algorithms, we use a Hamiltonian-driven unified framework. With such a framework, it is shown that there is a tradeoff between the communication burden and the control performance. We finally show that the developed policies exhibit Zeno-free behaviors. Simulation examples show the efficiency of the proposed framework along with quantifiable comparisons of the policies with different intermittent information.
Keyword:
Bandwidth
Dynamic programming
Optimal control
Mathematical model
Stability analysis
Multi-agent systems
Nonlinear dynamical systems
Adaptive dynamic programming (ADP)
communication bandwidth
control performance
Hamiltonian-driven framework
intermittent control
tradeoff
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期刊
IF:
10.5
论文数:
1.1W
被引数:
5.0W
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