By Topic

Connectionist approach to PID autotuning

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 $31
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

3 Author(s)
Ruano, A.E.B. ; Sch. of Electr. Eng. Sci., Wales Univ., Bangor, UK ; Fleming, P.J. ; Jones, D.I.

A connectionist method for autotuning PID controllers is proposed. This technique, which is applicable both in open and in closed loops, employs multilayer perceptrons to approximate the mappings between the identification measures of the plant and the optimal PID values. The neural network controller is designed to adapt to changing system structures and parameter values online. To achieve this objective, the network weighting coefficients are determined during an offline training phase. Simulation results are presented to illustrate the properties of the controller. In the proposed approach, multilayer perceptrons are employed for nonlinear function approximation. As a consequence, the neurons have a linear activation function in their output layer. It is shown that a new learning criterion can be defined for this class of multilayer perceptrons, which is commonly found in control systems applications. Comparisons of the standard and the reformulated criteria, using different training algorithms, show that the new formulation achieves a significant reduction in the number of iterations needed to converge to a local minimum.

Published in:

Control Theory and Applications, IEE Proceedings D  (Volume:139 ,  Issue: 3 )