Article ID | Journal | Published Year | Pages | File Type |
---|---|---|---|---|
9650555 | Engineering Applications of Artificial Intelligence | 2005 | 10 Pages |
Abstract
The work described in this paper aims at exploring the use of computational intelligence (CI) techniques for designing a Wiener-model controller to perform pH control. First, genetic algorithm (GA) is utilized to identify the static inverse titration relationship of a weak-acid strong-base titration process. The resulting model of the inverse neutralization equation then serves as the component in a Wiener model controller that linearizes the pH process. As the bulk of the system non-linearity is cancelled by the inverse model, a setpoint-weighted Proportional plus Integral plus Derivative (PID) controller is used to generate the control signal. A multi-objective evolutionary algorithm (MOEA) is employed to evolve a pareto optimal set of PID parameters in order to achieve the conflicting goals of fast rise time with small overshoots. Experimental results obtained from a laboratory-scale acid-base titration process are then presented to demonstrate the feasibility of the design methodology.
Related Topics
Physical Sciences and Engineering
Computer Science
Artificial Intelligence
Authors
W.W. Tan, F. Lu, A.P. Loh, K.C. Tan,