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∞.

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

scholarly journals Takagi-Sugeno Fuzzy Model of a One-Half Semiactive Vehicle Suspension: Lateral Approach

2015 ◽  
Vol 2015 ◽  
pp. 1-12 ◽  
Author(s):  
L. C. Félix-Herrán ◽  
D. Mehdi ◽  
J. J. Rodríguez-Ortiz ◽  
R. Ramírez-Mendoza ◽  
R. Soto
Keyword(s):  
Real Life ◽  
Fuzzy Model ◽  
Lateral Approach ◽  
Vehicle Suspension ◽  
Ground Vehicles ◽  
Actuator Dynamics ◽  
Fuzzy Membership Functions ◽  
Performance Indexes ◽  
And Performance ◽  
Takagi Sugeno

This work presents a novel semiactive model of a one-half lateral vehicle suspension. The contribution of this research is the inclusion of actuator dynamics (two magnetorheological nonlinear dampers) in the modelling, which means that more realistic outcomes will be obtained, because, in real life, actuators have physical limitations. Takagi-Sugeno (T-S) fuzzy approach is applied to a four-degree-of-freedom (4-DOF) lateral one-half vehicle suspension. The system has two magnetorheological (MR) dampers, whose numerical values come from a real characterization. T-S allows handling suspension’s components and actuator’s nonlinearities (hysteresis, saturation, and viscoplasticity) by means of a set of linear subsystems interconnected via fuzzy membership functions. Due to their linearity, each subsystem can be handled with the very well-known control theory, for example, stability and performance indexes (this is an advantage of the T-S approach). To the best of authors’ knowledge, reported work does not include the aforementioned nonlinearities in the modelling. The generated model is validated via a case of study with simulation results. This research is paramount because it introduces a more accurate (the actuator dynamics, a complex nonlinear subsystem) model that could be applied to one-half vehicle suspension control purposes. Suspension systems are extremely important for passenger comfort and stability in ground vehicles.

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.

Model‐predictive control based on Takagi‐Sugeno fuzzy model for electrical vehicles delayed model

2017 ◽  
Vol 11 (5) ◽  
pp. 918-934 ◽  
Author(s):  
Mohammad Hassan Khooban ◽  
Navid Vafamand ◽  
Taher Niknam ◽  
Tomislav Dragicevic ◽  
Frede Blaabjerg
Keyword(s):  
Model Predictive Control ◽  
Predictive Control ◽  
Fuzzy Model ◽  
Electrical Vehicles ◽  
Takagi Sugeno ◽  
Delayed Model
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