TY - JOUR
T1 - Codesign of Quantized Dynamic Output Feedback MPC for the Takagi-Sugeno Model
AU - Hu, Jianchen
AU - Li, Xingqi
AU - Xu, Zhanbo
AU - Pan, Hongguang
N1 - Publisher Copyright:
© 2005-2012 IEEE.
PY - 2023/7/1
Y1 - 2023/7/1
N2 - In this article, we present a codesign of measurement quantized dynamic output feedback model predictive control (DOFMPC) for the Takagi-Sugeno model with bounded disturbance. The system output is quantized by a dynamic quantizer before it is transmitted to the DOFMPC controller. Hence, we utilize the dynamic output feedback control law with a quantized output signal and consider the mixed input and quantized output constraint for the controller design. By optimizing the quantizer and controller parameters online, the control performance is enhanced. Moreover, we formulate a two-leveled optimizations, with the upper level optimizing the performance index and the lower level optimizing the soft constraint in a lexicographic order, for the codesign of the DOFMPC controller and dynamic quantizer. Thus, there are more degrees of freedom for tightening the soft constraints. The recursive feasibility and stability of the proposed approaches are guaranteed. The applicability of the proposed approach is illustrated by a simulation example.
AB - In this article, we present a codesign of measurement quantized dynamic output feedback model predictive control (DOFMPC) for the Takagi-Sugeno model with bounded disturbance. The system output is quantized by a dynamic quantizer before it is transmitted to the DOFMPC controller. Hence, we utilize the dynamic output feedback control law with a quantized output signal and consider the mixed input and quantized output constraint for the controller design. By optimizing the quantizer and controller parameters online, the control performance is enhanced. Moreover, we formulate a two-leveled optimizations, with the upper level optimizing the performance index and the lower level optimizing the soft constraint in a lexicographic order, for the codesign of the DOFMPC controller and dynamic quantizer. Thus, there are more degrees of freedom for tightening the soft constraints. The recursive feasibility and stability of the proposed approaches are guaranteed. The applicability of the proposed approach is illustrated by a simulation example.
KW - Measurement quantization
KW - model predictive control
KW - output feedback
KW - recursive feasibility
UR - https://www.scopus.com/pages/publications/85140785159
U2 - 10.1109/TII.2022.3215953
DO - 10.1109/TII.2022.3215953
M3 - 文章
AN - SCOPUS:85140785159
SN - 1551-3203
VL - 19
SP - 8049
EP - 8060
JO - IEEE Transactions on Industrial Informatics
JF - IEEE Transactions on Industrial Informatics
IS - 7
ER -