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Bayesian BrainProbabilistic Approaches to Neural Coding$
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Bayesian Brain: Probabilistic Approaches to Neural Coding

Kenji Doya, Shin Ishii, Alexandre Pouget, and Rajesh P.N. Rao

Print publication date: 2006

Print ISBN-13: 9780262042383

Published to MIT Press Scholarship Online: August 2013

DOI: 10.7551/mitpress/9780262042383.001.0001

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PRINTED FROM MIT PRESS SCHOLARSHIP ONLINE (www.mitpress.universitypressscholarship.com). (c) Copyright The MIT Press, 2020. All Rights Reserved. An individual user may print out a PDF of a single chapter of a monograph in MITSO for personal use.date: 06 April 2020

Optimal Control Theory

Optimal Control Theory

Chapter:
(p.268) (p.269) 12 Optimal Control Theory
Source:
Bayesian Brain
Author(s):

Emanuel Todorov

Publisher:
The MIT Press
DOI:10.7551/mitpress/9780262042383.003.0012

Optimal control theory is a mathematical discipline for studying the neural control of movement. This chapter presents a mathematical introduction to optimal control theory and discusses the following topics: Bellman equations, Hamilton-Jacobi-Bellman equations, Ricatti equations, and Kalman filter. It also examines the duality of optimal control and optimal estimation, and, finally, describes optimal control models and suggests future research directions.

Keywords:   optimal control theory, neural control, movement, Bellman equations, Ricatti equations, Kalman filter, optimal estimation

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