Decentralized Robust Interval Type-2 Fuzzy Model Predictive Control for Takagi-Sugeno Large-Scale Systems

TL;DR

This paper develops a decentralized robust interval type-2 fuzzy model predictive control method for large-scale Takagi-Sugeno systems, reducing computational costs and handling uncertainties and disturbances effectively.

Contribution

It introduces a novel decentralized MPC approach for nonlinear IT2 fuzzy large-scale systems with reduced online computational burden and improved robustness.

Findings

Significantly reduced online computational cost.

Effective control of large-scale systems with uncertainties.

Validated through two practical examples.

Abstract

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.