Design of predictive controllers by dynamic programming and neural networks

Chen Wen Yen, Mark L. Nagurka

Research output: Contribution to journalConference articlepeer-review

Abstract

This paper proposes a method for the design of predictive controllers for nonlinear systems, The method consists of two phases, a solution phase and a learning phase. In the solution phase, dynamic programming is applied to obtain a closed-loop control law. In the learning phase, neural networks are used to simulate the control law. This phase overcomes the "curse of dimensionality" problem that has often hindered the implementation of control laws generated by dynamic programming. Experimental results demonstrate the effectiveness of the method.

Original languageEnglish
Pages (from-to)1284-1289
Number of pages6
JournalProceedings of the American Control Conference
Volume2
StatePublished - 7 Nov 2003
Externally publishedYes
Event2003 American Control Conference - Denver, CO, United States
Duration: 4 Jun 20036 Jun 2003

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