TY - GEN
T1 - Extended self-tuning generalized predictive control with computation reduction focused on closed-loop characteristics
AU - Yanou, Akira
AU - Minami, Mamoru
AU - Matsuno, Takayuki
N1 - Funding Information:
by JSPS KAKENHI Grant
Funding Information:
This work was supported by JSPS KAKENHI Grant Number 24760337. The authors gratefully acknowledge reviewers’ comments.
PY - 2013
Y1 - 2013
N2 - This paper proposes a design method of extended self-tuning generalized predictive control (GPC) with computation reduction focused on closed-loop characteristics. The authors have extended GPC by coprime factorization and proposed the extended controller for constructing a strongly stable system. Moreover, the proposed controller is able to be designed to make the same steady state output as pre-designed system's steady state output even if feedback loop is cut. Although self-tuning controller is one of the control methods for systems with uncertainty, there is a problem that the computation of self-tuning GPC increases as design engineer takes long prediction horizon in the design parameters. Therefore this paper considers computation reduction for extended self-tuning GPC focused on closed-loop characteristics. The validity of the proposed method is shown by numerical simulation.
AB - This paper proposes a design method of extended self-tuning generalized predictive control (GPC) with computation reduction focused on closed-loop characteristics. The authors have extended GPC by coprime factorization and proposed the extended controller for constructing a strongly stable system. Moreover, the proposed controller is able to be designed to make the same steady state output as pre-designed system's steady state output even if feedback loop is cut. Although self-tuning controller is one of the control methods for systems with uncertainty, there is a problem that the computation of self-tuning GPC increases as design engineer takes long prediction horizon in the design parameters. Therefore this paper considers computation reduction for extended self-tuning GPC focused on closed-loop characteristics. The validity of the proposed method is shown by numerical simulation.
KW - Closed-loop gain
KW - Coprime factorization
KW - Data reduction.
KW - Generalized predictive control
KW - Self-tuning control
UR - http://www.scopus.com/inward/record.url?scp=84885809894&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=84885809894&partnerID=8YFLogxK
U2 - 10.3182/20130703-3-FR-4038.00112
DO - 10.3182/20130703-3-FR-4038.00112
M3 - Conference contribution
AN - SCOPUS:84885809894
SN - 9783902823373
T3 - IFAC Proceedings Volumes (IFAC-PapersOnline)
SP - 51
EP - 56
BT - 11th IFAC International Workshop on Adaptation and Learning in Control and Signal Processing, ALCOSP 2013 - Proceedings
PB - IFAC Secretariat
T2 - 11th IFAC International Workshop on Adaptation and Learning in Control and Signal Processing, ALCOSP 2013
Y2 - 3 July 2013 through 5 July 2013
ER -