  In this manuscript, decentralized robust interval type-2 fuzzy model
predictive control for Takagi-Sugeno large-scale systems is studied. The
mentioned large-scale system consists a number of interval type-2 (IT2) fuzzy
Takagi-Sugeno (T-S) subsystems. An important matter and necessities that limit
the practical application of model predictive control are the online
computational cost and burden of the existence frameworks. For model predictive
control of T-S fuzzy large-scale systems, the online computational burden is
even worse and in some cases, they cannot be solved in an expected time.
Especially for severe large-scale systems with disturbances, existing model
predictive control of T-S fuzzy large-scale systems usually leads to a very
conservative solution. So, researchers have many challenges and difficulties in
finding a reasonable solution in a short time. Although, more relaxed results
can be achieved by the proposed fuzzy model predictive control approach which
adopts T-S large-scale systems with nonlinear subsystems, many restrictions are
not considered in these approaches. In this paper, challenges are solved and
the MPC is designed for a nonlinear IT2 fuzzy large-scale system with
uncertainties and disturbances. Besides, online optimization problem is solved
and results are proposed. Consequently, online computational cost of the
optimization problem is reduced considerably. At the end, by two practical
examples, the effectiveness of the proposed algorithm is illustrated.
