title: A fuzzy model based adaptive PID controller design for nonlinear and uncertain processes.
abstract: We develop a novel adaptive tuning method for classical proportional-integral-derivative (PID) controller to control nonlinear processes to adjust PID gains, a problem which is very difficult to overcome in the classical PID controllers. By incorporating classical PID control, which is well-known in industry, to the control of nonlinear processes, we introduce a method which can readily be used by the industry. In this method, controller design does not require a first principal model of the process which is usually very difficult to obtain. Instead, it depends on a fuzzy process model which is constructed from the measured input-output data of the process. A soft limiter is used to impose industrial limits on the control input. The performance of the system is successfully tested on the bioreactor, a highly nonlinear process involving instabilities. Several tests showed the method's success in tracking, robustness to noise, and adaptation properties. We as well compared our system's performance to those of a plant with altered parameters with measurement noise, and obtained less ringing and better tracking. To conclude, we present a novel adaptive control method that is built upon the well-known PID architecture that successfully controls highly nonlinear industrial processes, even under conditions such as strong parameter variations, noise, and instabilities.
authors.affiliation: Department of Electrical and Electronics Engineering, Ege University, 35100 Bornova, Izmir, Turkey. Electronic address: aydogan.savran@ege.edu.tr.
authors.forename: Aydogan
authors.initials: A
authors.lastname: Savran
authors.affiliation: Department of Electrical and Electronics Engineering, Ege University, 35100 Bornova, Izmir, Turkey. Electronic address: gokalp.kahraman@ege.edu.tr.
authors.forename: Gokalp
authors.initials: G
authors.lastname: Kahraman
keywords: Adaptive; BPTT; Control; Fuzzy; LM; PID; Prediction; Soft limiter