TY - JOUR
T1 - Multivariable Nonadaptive Controller Design
AU - Said Saab, Samer
AU - Hauser, Michael
AU - Ray, Asok
AU - Saab, Samer Said
N1 - Funding Information:
Manuscript received December 18, 2019; revised March 9, 2020 and April 27, 2020; accepted May 14, 2020. Date of publication June 4, 2020; date of current version March 22, 2021. The work of Samer Said Saab Jr. was supported through the Walker Fellowship, offered by the Applied Research Laboratory at The Pennsylvania State University. (Samer Said Saab Jr. and Michael Hauser contributed equally to this work.) (Corresponding author: Samer Said Saab.) Samer Said Saab Jr. is with the Department of Electrical Engineering, Pennsylvania State University, University Park, PA 16802 USA (e-mail: sys5880@psu.edu).
Publisher Copyright:
© 1982-2012 IEEE.
PY - 2021/7
Y1 - 2021/7
N2 - Although proportional-integral-derivative control remains one of the most common control schemes used in industry, its tuning still remains inadequately understood in many applications. This task becomes much more challenging when applied to multi-input multi-output (MIMO) systems. This article presents the design of a discrete-time robust multivariable (nonadaptive) tracking controller that comes with a simple structure, requires very limited information on the plant model, and is relatively easy to tune. In addition to being easy to tune and implement, an objective of this controller is to deal with a class of large-scale systems with complex dynamics. We analytically demonstrate the robustness and convergence of the closed-loop system for a class of MIMO linear time-varying systems. The overall superiority of the proposed controller is experimentally validated on a Barrett robot arm in a laboratory environment. The article also provides a stochastic framework of the general setting of the controller. Within this framework, two minimum mean square error optimal solutions of the controller are provided; one is designed for the case where the number of inputs is not greater than the number of outputs, and the other is for the antithesis.
AB - Although proportional-integral-derivative control remains one of the most common control schemes used in industry, its tuning still remains inadequately understood in many applications. This task becomes much more challenging when applied to multi-input multi-output (MIMO) systems. This article presents the design of a discrete-time robust multivariable (nonadaptive) tracking controller that comes with a simple structure, requires very limited information on the plant model, and is relatively easy to tune. In addition to being easy to tune and implement, an objective of this controller is to deal with a class of large-scale systems with complex dynamics. We analytically demonstrate the robustness and convergence of the closed-loop system for a class of MIMO linear time-varying systems. The overall superiority of the proposed controller is experimentally validated on a Barrett robot arm in a laboratory environment. The article also provides a stochastic framework of the general setting of the controller. Within this framework, two minimum mean square error optimal solutions of the controller are provided; one is designed for the case where the number of inputs is not greater than the number of outputs, and the other is for the antithesis.
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U2 - 10.1109/TIE.2020.2998753
DO - 10.1109/TIE.2020.2998753
M3 - Article
AN - SCOPUS:85103412694
VL - 68
SP - 6181
EP - 6191
JO - IEEE Transactions on Industrial Electronics
JF - IEEE Transactions on Industrial Electronics
SN - 0278-0046
IS - 7
M1 - 9108578
ER -