Distribution modeling of nonlinear inverse controllers under a Bayesian framework.
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.
Herzallah
Randa
R
Department of Mechatronics Engineering, Al-Balqa' Applied University, Amman 11185, Jordan. randa_herzal@hotmail.com
Lowe
David
D
D000465:Algorithms; D001185:Artificial Intelligence; D001499:Bayes Theorem; D003198:Computer Simulation; D005246:Feedback; D015233:Models, Statistical; D017711:Nonlinear Dynamics; D016008:Statistical Distributions; D013598:Systems Theory