A Bayesian approach to modeling the conditional density of the inverse controller

Randa Herzallah; David Lowe

doi:10.1109/CCA.2004.1387310

A Bayesian approach to modeling the conditional density of the inverse controller

Randa Herzallah, David Lowe

Computer Science Research Group

Research output: Chapter in Book/Published conference output › Conference publication

Abstract

The inverse controller is traditionally assumed to be a deterministic function. This paper presents a pedagogical methodology for estimating the stochastic model of the inverse controller. The proposed method is based on Bayes' theorem. Using Bayes' rule to obtain the stochastic model of the inverse controller allows the use of knowledge of uncertainty from both the inverse and the forward model in estimating the optimal control signal. The paper presents the methodology for general nonlinear systems. For illustration purposes, the proposed methodology is applied to linear Gaussian systems.

Original language	English
Title of host publication	Proceedings of the IEEE International Conference on Control Applications, 2004
Publisher	IEEE
Pages	788-793
Number of pages	6
Volume	1
ISBN (Print)	0-7803-8633-7
DOIs	https://doi.org/10.1109/CCA.2004.1387310
Publication status	Published - 2004
Event	2004 IEEE International Conference on Control Applications - Taipei, United Kingdom Duration: 2 Sept 2004 → 4 Sept 2004

Conference

Conference	2004 IEEE International Conference on Control Applications
Country/Territory	United Kingdom
City	Taipei
Period	2/09/04 → 4/09/04

Access to Document

10.1109/CCA.2004.1387310

Cite this

@inproceedings{ebbb8b34d6ab4dc6bfe94d1e3b02aea4,

title = "A Bayesian approach to modeling the conditional density of the inverse controller",

abstract = "The inverse controller is traditionally assumed to be a deterministic function. This paper presents a pedagogical methodology for estimating the stochastic model of the inverse controller. The proposed method is based on Bayes' theorem. Using Bayes' rule to obtain the stochastic model of the inverse controller allows the use of knowledge of uncertainty from both the inverse and the forward model in estimating the optimal control signal. The paper presents the methodology for general nonlinear systems. For illustration purposes, the proposed methodology is applied to linear Gaussian systems.",

author = "Randa Herzallah and David Lowe",

year = "2004",

doi = "10.1109/CCA.2004.1387310",

language = "English",

isbn = "0-7803-8633-7",

volume = "1",

pages = "788--793",

booktitle = "Proceedings of the IEEE International Conference on Control Applications, 2004",

publisher = "IEEE",

address = "United States",

note = "2004 IEEE International Conference on Control Applications ; Conference date: 02-09-2004 Through 04-09-2004",

}

TY - GEN

T1 - A Bayesian approach to modeling the conditional density of the inverse controller

AU - Herzallah, Randa

AU - Lowe, David

PY - 2004

Y1 - 2004

N2 - The inverse controller is traditionally assumed to be a deterministic function. This paper presents a pedagogical methodology for estimating the stochastic model of the inverse controller. The proposed method is based on Bayes' theorem. Using Bayes' rule to obtain the stochastic model of the inverse controller allows the use of knowledge of uncertainty from both the inverse and the forward model in estimating the optimal control signal. The paper presents the methodology for general nonlinear systems. For illustration purposes, the proposed methodology is applied to linear Gaussian systems.

AB - The inverse controller is traditionally assumed to be a deterministic function. This paper presents a pedagogical methodology for estimating the stochastic model of the inverse controller. The proposed method is based on Bayes' theorem. Using Bayes' rule to obtain the stochastic model of the inverse controller allows the use of knowledge of uncertainty from both the inverse and the forward model in estimating the optimal control signal. The paper presents the methodology for general nonlinear systems. For illustration purposes, the proposed methodology is applied to linear Gaussian systems.

UR - http://www.scopus.com/inward/record.url?scp=17744381216&partnerID=8YFLogxK

UR - http://ieeexplore.ieee.org/xpl/articleDetails.jsp?arnumber=1387310

U2 - 10.1109/CCA.2004.1387310

DO - 10.1109/CCA.2004.1387310

M3 - Conference publication

AN - SCOPUS:17744381216

SN - 0-7803-8633-7

VL - 1

SP - 788

EP - 793

BT - Proceedings of the IEEE International Conference on Control Applications, 2004

PB - IEEE

T2 - 2004 IEEE International Conference on Control Applications

Y2 - 2 September 2004 through 4 September 2004

ER -

A Bayesian approach to modeling the conditional density of the inverse controller

Abstract

Conference

Access to Document

Other files and links

Fingerprint

Cite this