Distribution Modeling of Nonlinear Inverse Controllers Under a Bayesian Framework

Author

Herzallah, Randa ; Lowe, David

Author_Institution

Dept. of Mechatronics Eng., Al-Balqa´´ Appl. Univ., Amman

Volume

18

Issue

1

fYear

2007

Firstpage

107

Lastpage

114

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

Keywords

Bayes methods; MIMO systems; nonlinear control systems; optimal control; stochastic systems; Bayesian Framework; MIMO; SISO; deterministic function; distribution modeling; multiple-input-multiple-output system; nonlinear inverse control; nonlinear single-input-single-output system; optimal control signal; Adaptive control; Bayesian methods; Inverse problems; Neural networks; Nonlinear systems; Optimal control; Parameter estimation; Programmable control; Stochastic processes; Uncertainty; Distribution modelling; inverse controller; neural networks; stochastic systems; uncertainty; Algorithms; Artificial Intelligence; Bayes Theorem; Computer Simulation; Feedback; Models, Statistical; Nonlinear Dynamics; Statistical Distributions; Systems Theory;

fLanguage

English

Journal_Title

Neural Networks, IEEE Transactions on

Publisher

ieee

ISSN

1045-9227

Type

jour

DOI

10.1109/TNN.2006.883721

Filename

4049822