The aim of this work is to present an extension of the closed-loop model-reference robust tuning (MoReRT) methodology to proportional integral and proportional integral derivative control algorithms enhanced with two input filters (set-point and feedback signal filters) for control of stable, integrating, inverse response, and unstable controlled processes. The method is based on the use of target models that include two design parameters (closed-loop dominant poles relative speed and damping). The control system performance/robustness trade-off is analyzed to reduce the design parameters to only one directly related with the control system robustness measured with the maximum sensitivity (MS). The proposed design methodology takes into consideration the control system load-disturbance rejection, set-point tracking, control effort smoothness, measurement of high frequency noise attenuation, and robustness to changes on the controlled process dynamics. The incorporation of these two input filters allows taking into consideration two, rarely included, industrial application oriented features: high-frequency roll-off and lack of control effort abrupt changes. Comparisons and examples show that the proposed design strategy can be applied to diverse controlled process models to obtain robust control systems that produce smooth controller outputs without abrupt changes, and with performance comparable to or better than other tuning procedures. © 2013 American Chemical Society.
|Journal||Industrial and Engineering Chemistry Research|
|Publication status||Published - 26 Dec 2013|