Title :
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
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;
Journal_Title :
Neural Networks, IEEE Transactions on
DOI :
10.1109/TNN.2006.883721
