Experimental performance portrait based optimal controller tuning

Abstract

The performance portrait based optimal controller tuning may be considered as a generalization of the well known method by Ziegler and Nichols [1] that integrated an experimental plant identification with the controller tuning. Up to now, the performance portrait method (PPM) has been presented as a tool for robust and optimal controller tuning [4]-[6]. This paper presents its application as a new experimental identification and controller tuning tool built upon a precomputed closed loop performance portrait. Problems of choosing an appropriate plant model for evaluating experiments carried out under a chosen controller by using a performance portrait are discussed, together with problems of performance portrait quantization and rules for determining an optimal experiment scenario.