Approximate dynamic programming-based approaches for input–output data-driven control of nonlinear processes

We propose two approximate dynamic programming (ADP)-based strategies for control of nonlinear processes using input–output data. In the first strategy, which we term ‘ J -learning,’ one builds an empirical nonlinear model using closed-loop test data and performs dynamic programming with it to deriv...

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Vydáno v:	Automatica (Oxford) Ročník 41; číslo 7; s. 1281 - 1288
Hlavní autoři:	Lee, Jong Min, Lee, Jay H.
Médium:	Journal Article
Jazyk:	angličtina
Vydáno:	Oxford Elsevier Ltd 01.07.2005 Elsevier
Témata:	[formula omitted]-learning Applied sciences Approximate dynamic programming Computer science; control theory; systems Control theory. Systems Exact sciences and technology NARX model Nonlinear model identification Nonlinear model predictive control Process control. Computer integrated manufacturing Reinforcement learning Nonlinear model identification Approximate dynamic programming Q -learning Nonlinear model predictive control NARX model Reinforcement learning Q-learning Process control Non linear control Autoregressive model Empirical model Modeling Dynamic test Model predictive control Model matching Optimal control Non linear model System identification Dynamic programming Learning algorithm Predictive control
ISSN:	0005-1098, 1873-2836
On-line přístup:	Získat plný text
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