IEEE Transactions on Neural Networks and Learning Systems · 2014 · 409 citations · 39 references
Natural SciencesMathematicsDynamical SystemsOptimizationProcess ControlAdaptive ControlControl TheoryController DesignDynamic ProgrammingNonlinear ControlRobust ControlAdaptive Control Of Nonlinear SystemsAdaptive Control Of Uncertain SystemsNonlinear Control SystemsNonlinear SystemsPower System ControlRobust Control DesignSmall-gain TheoremsStabilization
This paper studies the robust optimal control design for a class of uncertain nonlinear systems from a perspective of robust adaptive dynamic programming (RADP). The objective is to fill up a gap in the past literature of adaptive dynamic programming (ADP) where dynamic uncertainties or unmodeled dynamics are not addressed. A key strategy is to integrate tools from modern nonlinear control theory, such as the robust redesign and the backstepping techniques as well as the nonlinear small-gain theorem, with the theory of ADP. The proposed RADP methodology can be viewed as an extension of ADP to uncertain nonlinear systems. Practical learning algorithms are developed in this paper, and have been applied to the controller design problems for a jet engine and a one-machine power system.
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Reinforcement Learning: An Introduction
Richard S. Sutton, Andy Barto · IEEE Transactions on Neural Networks · 1998 · 27.1K citations
Learning to Predict by the Methods of Temporal Differences
Richard S. Sutton · Machine Learning · 1988 · 4K citations · Full text
Learning to predict by the methods of temporal differences
Richard S. Sutton · Machine Learning · 1988 · 2.8K citations · Full text