A PID autotuning method is presented to exploit the knowledge of multiple frequency response points while employing simple and well assessed tuning rules (e.g. based on Internal Model Control). This is achieved by selecting, among the available frequency response points, the 'best one' for indices that are compared based on also other points. Hence, the proposed method can ensure for example (local) robustness properties while being just slightly more complex than the single point tuning. Peculiar to the method is the use of the 'contextual' approach, that provides both the controller parameters, and a process model inherently precise near the obtained cutoff frequency. This allows to reliably compute tuning quality indices that require a simulation of the closed loop. The proposed technique can thus assist the user in evaluating the different tunings produced by centering the synthesis on each point. A demonstration of the procedure is given on a set of models taken from a literature benchmark, to witness its usefulness and applicability.

Applying simple PID tuning rules with extended frequency response knowledge

BRANKOVIC, AIDA;LEVA, ALBERTO
2016-01-01

Abstract

A PID autotuning method is presented to exploit the knowledge of multiple frequency response points while employing simple and well assessed tuning rules (e.g. based on Internal Model Control). This is achieved by selecting, among the available frequency response points, the 'best one' for indices that are compared based on also other points. Hence, the proposed method can ensure for example (local) robustness properties while being just slightly more complex than the single point tuning. Peculiar to the method is the use of the 'contextual' approach, that provides both the controller parameters, and a process model inherently precise near the obtained cutoff frequency. This allows to reliably compute tuning quality indices that require a simulation of the closed loop. The proposed technique can thus assist the user in evaluating the different tunings produced by centering the synthesis on each point. A demonstration of the procedure is given on a set of models taken from a literature benchmark, to witness its usefulness and applicability.
2016
2016 IEEE 55th Conference on Decision and Control, CDC 2016
9781509018376
9781509018376
Artificial Intelligence; Decision Sciences (miscellaneous); Control and Optimization
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11311/1009156
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