scholarly journals Robust Fuzzy Model Predictive Control of Discrete-Time Takagi–Sugeno Systems With Nonlinear Local Models

2018 ◽  
Vol 26 (5) ◽  
pp. 2915-2925 ◽  
Author(s):  
Long Teng ◽  
Youyi Wang ◽  
Wenjian Cai ◽  
Hua Li
Keyword(s):  
Model Predictive Control ◽  
Predictive Control ◽  
Discrete Time ◽  
Fuzzy Model ◽  
Local Models ◽  
Takagi Sugeno

scholarly journals Decentralized robust interval type-2 fuzzy model predictive control for Takagi–Sugeno large-scale systems

Automatika ◽  
2021 ◽  
Vol 63 (1) ◽  
pp. 49-63
Author(s):  
Mohammad Sarbaz ◽  
Iman Zamani ◽  
Mohammad Manthouri ◽  
Asier Ibeas
Keyword(s):  
Model Predictive Control ◽  
Predictive Control ◽  
Large Scale ◽  
Fuzzy Model ◽  
Large Scale Systems ◽  
Interval Type ◽  
Takagi Sugeno

Fuzzy Based Model Predictive Control for Uncertain Nonlinear System

2011 ◽  
Vol 383-390 ◽  
pp. 2404-2410
Author(s):  
Li Xu ◽  
Fei Liu
Keyword(s):  
Nonlinear System ◽  
Model Predictive Control ◽  
Predictive Control ◽  
Fuzzy Model ◽  
Linear Matrix ◽  
Uncertain Nonlinear System ◽  
Predictive Controller ◽  
The Stability ◽  
Takagi Sugeno ◽  
Parameter Dependent

In this paper, a model predictive control (MPC) scheme is investigated for uncertain nonlinear system with time delay and input constraint. First, the Takagi-Sugeno (T-S) fuzzy model is used to approximate the dynamics of nonlinear processes and the parallel distributed compensation (PDC) controllers which are parameter dependent and mirror the structure of the T-S plant model are proposed. Then a novel feedback PDC predictive controller obtained from the linear matrix inequality (LMI) solutions which can guarantee the stability of the closed-loop overall fuzzy system is put forward. Finally, a numerical example is provided to demonstrate the effectiveness and feasibility of the proposed method.

Offline FMPC Applied to the 3SSC Boost Converter

2020 ◽  
Author(s):  
Thalita B. S. Moreira ◽  
Marcus V. S. Costa ◽  
F. G. Nogueira
Keyword(s):  
Model Predictive Control ◽  
Predictive Control ◽  
Fuzzy Model ◽  
Boost Converter ◽  
Lookup Table ◽  
Control Law ◽  
Fuzzy Membership Functions ◽  
And Performance ◽  
Augmented State ◽  
Takagi Sugeno

This article develops a novel application for fuzzy model predictive control (FMPC), applying this control law to a three state-switching cell (3SSC) boost converter. The converter is modeled using augmented state space equations through a polytopic approach, associated with fuzzy membership functions in order to reject disturbances caused by the change in the operating point. Furthermore, the FMPC combines Model Predictive Control (MPC), Takagi-Sugeno (T-S) and Parallel-Distributed Compensation (PDC) fuzzy methodologies. In addition, an offline approach for the FMPC is presented, whose gains are obtained via stability ellipsoids and stored in a lookup-table. The obtained results highlight the performance of the proposed method in comparison with the benchmark controller, through analysis of time responses, stability ellipsoids and performance index J∞.

Sign in / Sign up

Export Citation Format

Share Document