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The intelligent critic framework for advanced optimal control
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DOI：10.1007/s10462-021-10118-9.png)
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摘要
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The idea of optimization can be regarded as an important basis of many disciplines and hence is extremely useful for a large number of research fields, particularly for artificial-intelligence-based advanced control design. Due to the difficulty of solving optimal control problems for general nonlinear systems, it is necessary to establish a kind of novel learning strategies with intelligent components. Besides, the rapid development of computer and networked techniques promotes the research on optimal control within discrete-time domain. In this paper, the bases, the derivation, and recent progresses of critic intelligence for discrete-time advanced optimal control design are presented with an emphasis on the iterative framework. Among them, the so-called critic intelligence methodology is highlighted, which integrates learning approximators and the reinforcement formulation.
Keyword:
Advanced optimal control
Dynamic systems
Intelligent critic
期刊
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13.9
论文数:
6.1K
被引数:
1.9W
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