By Topic

A neural network controller for systems with unmodeled dynamics with applications to wastewater treatment

Sign In

Cookies must be enabled to login.After enabling cookies , please use refresh or reload or ctrl+f5 on the browser for the login options.

Formats Non-Member Member
$31 $13
Learn how you can qualify for the best price for this item!
Become an IEEE Member or Subscribe to
IEEE Xplore for exclusive pricing!
close button

puzzle piece

IEEE membership options for an individual and IEEE Xplore subscriptions for an organization offer the most affordable access to essential journal articles, conference papers, standards, eBooks, and eLearning courses.

Learn more about:

IEEE membership

IEEE Xplore subscriptions

2 Author(s)
Spall, J.C. ; Appl. Phys. Lab., Johns Hopkins Univ., Laurel, MD, USA ; Cristion, John A.

This paper considers the use of neural networks (NNs) in controlling a nonlinear, stochastic system with unknown process equations. The approach here is based on using the output error of the system to train the NN controller without the need to assume or construct a separate model (NN or other type) for the unknown process dynamics. To implement such a direct adaptive control approach, it is required that connection weights in the NN be estimated while the system is being controlled. As a result of the feedback of the unknown process dynamics, however, it is not possible to determine the gradient of the loss function for use in standard (backpropagation-type) weight estimation algorithms. Therefore, this paper consider's the use of a new stochastic approximation algorithm for this weight estimation, which is based on a “simultaneous perturbation” gradient approximation that only requires the system output error. It is shown that this algorithm can greatly enhance the efficiency over more standard stochastic approximation algorithms based on finite-difference gradient approximations. The approach is illustrated on a simulated wastewater treatment system with stochastic effects and nonstationary dynamics

Published in:

Intelligent Control, 1994., Proceedings of the 1994 IEEE International Symposium on

Date of Conference:

16-18 Aug 1994