Abstract
Here, decentralized robust interval type-2 (IT2) fuzzy model predictive control (MPC) for Takagi–Sugeno (T-S) large-scale systems is studied. The large-scale system consists of many IT2 fuzzy T–S subsystems. Important necessities that limit the practical application of MPC are the online computational cost and burden of the frameworks. For MPC of T–S fuzzy large-scale systems, the online computational burden is even worse, and in some cases, they cannot be solved timely. Especially for severe, large-scale systems with disturbances, the MPC of T–S fuzzy large-scale systems usually give a conservative solution. So, researchers have many challenges and in finding a reasonable solution in a short time. Although more comfortable results can be achieved by the proposed fuzzy MPC approach, which adopts T–S large-scale systems with nonlinear subsystems, many restrictions are not considered. In this paper, challenges are solved, and the MPC is designed for a nonlinear IT2 fuzzy large-scale system with uncertainties and disturbances. Besides, the online optimization problem is solved, and results are proposed. Consequently, the online computational cost of the optimization problem is reduced considerably. Finally, the effectiveness of the proposed algorithm is illustrated with two practical examples.
Highlights
Since years ago, the main task in engineering has been control of processes like mechanical engineering, electrical engineering, chemical engineering, or so
Designing the model predictive control (MPC) for fuzzy large-scale system with nonlinear and complex dynamic considering the uncertainties and disturbances has turned to a real challenge and almost in all systems it is impossible to reach a non-conservative solution, solving the online optimization problem
Remark 4: by proposing the Theorem 2, the concept of the robust positively invariant and constraint set are achieved through solving a linear matrix inequalities (LMIs), and it will be ensured that trajectories of the large-scale system are stable robustly
Summary
The main task in engineering has been control of processes like mechanical engineering, electrical engineering, chemical engineering, or so. In [28] new MPC is contrived for polytypic linear parameter varying systems and paradigm used is adopted in gain scheduling It seems that MPC for interval type-2 fuzzy large-scale systems with persistent disturbances has not been studied yet and several problems remind unsolved. Designing the MPC for fuzzy large-scale system with nonlinear and complex dynamic considering the uncertainties and disturbances has turned to a real challenge and almost in all systems it is impossible to reach a non-conservative solution, solving the online optimization problem. In this paper, by the proposed method, this challenge is solved and the MPC is designed for a nonlinear fuzzy large-scale system with uncertainties and disturbances.
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