Encyclopedia of Systems and Control

2015 Edition
| Editors: John Baillieul, Tariq Samad

Neural Control and Approximate Dynamic Programming

Reference work entry
DOI: https://doi.org/10.1007/978-1-4471-5058-9_224

Abstract

There has been great interest recently in “universal model-free controllers” that do not need a mathematical model of the controlled plant, but mimic the functions of biological processes to learn about the systems they are controlling online, so that performance improves automatically. Neural network (NN) control has had two major thrusts: approximate dynamic programming, which uses NN to approximately solve the optimal control problem, and NN in closed-loop feedback control.

Keywords

Adaptive control Learning systems Neural networks Optimal control Reinforcement learning 
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Notes

Acknowledgements

This material is based upon the work supported by NSF. Grant Number: ECCS-1128050, ARO. Grant Number: W91NF-05-1-0314, AFOSR. Grant Number: FA9550-09-1-0278.

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Copyright information

© Springer-Verlag London 2015

Authors and Affiliations

  1. 1.Arlington Research InstituteUniversity of TexasFort WorthUSA
  2. 2.Center for Control, Dynamical Systems and Computation (CCDC)University of CaliforniaSanta BarbaraUSA