Automatic tuning of PID and gain scheduling PID controllers by a derandomized evolution strategy

Abstract

This paper evaluates an evolution strategy to tune conventional proportional plus integral plus derivative (PID) and gain scheduling PID control algorithms. The approach deals with the utilization of an evolution strategy with learning acceleration by derandomized mutative step-size control using accumulated information. This technique is useful to obtain the following characteristics: (1) freedom of choice of a performance index, (2) increase of the convergence speed of evolution strategies to get a local minimum to determine controller design parameters, and (3) flexibility and robustness in the automatic design of controllers. Performance analysis and experimental results are carried out using a laboratory scale nonlinear process fan and plate. The practical prototype contains features such as nonminimum phase, dead time, resonant, and turbulent disturbance behavior that motivate the utilization of intelligent control techniques.