Distribution modeling of nonlinear inverse controllers under a Bayesian framework

Randa Herzallah; David Lowe

doi:10.1109/TNN.2006.883721

Distribution modeling of nonlinear inverse controllers under a Bayesian framework

IEEE Trans Neural Netw. 2007 Jan;18(1):107-14. doi: 10.1109/TNN.2006.883721.

Authors

Randa Herzallah¹, David Lowe

Affiliation

¹ Department of Mechatronics Engineering, Al-Balqa' Applied University, Amman 11185, Jordan. randa_herzal@hotmail.com

PMID: 17278465
DOI: 10.1109/TNN.2006.883721

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 and is demonstrated on nonlinear single-input-single-output (SISO) and multiple-input-multiple-output (MIMO) examples.

MeSH terms

Algorithms*
Artificial Intelligence*
Bayes Theorem*
Computer Simulation
Feedback
Models, Statistical*
Nonlinear Dynamics*
Statistical Distributions
Systems Theory