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Data‐Driven Controller Tuning for Nonminimum Phase Plants with Stability Constraints
Author(s) -
MATSUO RYOTA,
YUBAI KAZUHIRO,
YASHIRO DAISUKE,
HIRAI JUNJI
Publication year - 2016
Publication title -
electrical engineering in japan
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.136
H-Index - 28
eISSN - 1520-6416
pISSN - 0424-7760
DOI - 10.1002/eej.22880
Subject(s) - control theory (sociology) , controller (irrigation) , stability (learning theory) , computation , open loop controller , reference model , control engineering , computer science , data driven , loop (graph theory) , closed loop , engineering , control (management) , mathematics , algorithm , software engineering , combinatorics , artificial intelligence , machine learning , agronomy , biology
SUMMARY This paper addresses the model reference control problem, which is a typical control problem found in data‐driven controller tuning methods. For nonminimum phase plants, the unstable zeros of the plant should be included in the reference to avoid destabilization of the resulting closed‐loop system and improve tracking performance. First, we propose a data‐driven controller tuning method with closed‐loop stability taken into consideration and with the tuned controller parameters in the time domain. If the plant has unstable zero(s), the proposed method would not lead to destabilizing controller in the worst case. Closed‐loop stability is checked using linear inequalities described with input/output data. This contributes to reducing computation in the proposed method. Moreover, this paper proposes a data‐driven controller tuning method for nonminimum phase plants estimating the unstable zero(s) using a flexible reference model at each parameter update and reflecting them into the resulting reference model. The effectiveness of the proposed method is confirmed through numerical experiments.