@article{MAKHILLIJSSCEA202013128776,
    title = {Distributed Model Predictive Control Based on New Cooperative Optimization Strategy for
Nonlinear Large Scale Uncertain Systems},
    journal = {International Journal of System Signal Control and Engineering Application},
    volume = {13},
    number = {1},
    pages = {1-17},
    year = {2020},
    issn = {1997-5422},
    doi = {ijssceapp.2020.1.17},
    url = {https://makhillpublications.co/view-article.php?issn=1997-5422&doi=ijssceapp.2020.1.17},
    author = {Amin and},
    keywords = {Cooperative optimization strategy,distributed extended dynamic matrix control,distributed adaptive generalized predictive control,equilibrium,trajectory},
    abstract = {In this study two linear cooperative Distributed
constrained Model Predictive Control (DMPC)
approaches are proposed to control the uncertain
nonlinear interconnected large scale systems. In these
approaches a proposed novel cooperative optimization
strategy is employed that its advantage is to improve the
centralized global cost function of each local controllers
which decreases the control efforts, cost function values
and convergence time compared to typical cooperative
DMPCs which is demonstrated via. simulation results of
a typical nonlinear large scale system. In proposed
approaches two reconstructed linear distributed
constrained model predictive controllers; Distributed
Extended Dynamic Matrix Control (DEDMC) and
Adaptive Generalized Predictive Control (DAGPC) are
presented to control the uncertain nonlinear large scale
systems by compensation of the mismatch between
linearized and nonlinear models. The advantage of
proposed controllers is their less complexity compared to
fully nonlinear DMPCs. In DEDMC, the mismatch
between linearized and nonlinear models is considered as
a disturbance and in DAGPC this mismatch is
compensated using online identification of the linearized
model. The typical linear algorithms like distributed DMC
leads to an unstable closed-loop response if the reference
trajectory is a little far from the equilibrium point while
this problem will be partially solved using the proposed
DEDMC and will be completely solved using the
proposed DAGPC even if the reference trajectory is too
far from the equilibrium point. The performance of
proposed approaches are demonstrated through simulation
of a typical uncertain nonlinear large scale system.}
    }