scholarly journals Design of an Unmatching Observer-Based Controller for Discrete-Time Fuzzy Systems with Time Delay

2020 ◽  
Vol 2020 ◽  
pp. 1-11
Author(s):  
Zejian Zhang ◽  
Dawei Wang
Keyword(s):  
Time Delay ◽  
Discrete Time ◽  
Fuzzy Systems ◽  
Fuzzy Controller ◽  
Controller Design ◽  
Fuzzy Model ◽  
Membership Functions ◽  
Design Flexibility ◽  
Control Scheme ◽  
The Stability

The problem of an unmatching observer-based controller design for discrete-time fuzzy systems with time delay is investigated, in which the fuzzy controller shares different membership functions from the fuzzy model. The objective is to design a state observer and unmatching fuzzy controller such that the discrete closed-loop system with time delay is asymptotically stable. A sufficient condition that contains the information of the membership functions of fuzzy model and fuzzy controller for the stabilization via an unmatching observer-based output feedback is presented. The proposed control scheme is well capable of enhancing the design flexibility, and the stability condition is less conservative. Three numerical examples are given to illustrate the effectiveness and advantages of the proposed method.

scholarly journals Actuator Saturated Fuzzy Controller Design for Interval Type-2 Takagi-Sugeno Fuzzy Models with Multiplicative Noises

Processes ◽  
2021 ◽  
Vol 9 (5) ◽  
pp. 823
Author(s):  
Wen-Jer Chang ◽  
Yu-Wei Lin ◽  
Yann-Horng Lin ◽  
Chin-Lin Pen ◽  
Ming-Hsuan Tsai
Keyword(s):  
Nonlinear Systems ◽  
Fuzzy Controller ◽  
Controller Design ◽  
Actuator Saturation ◽  
Design Method ◽  
Fuzzy Model ◽  
The Stability ◽  
Interval Type ◽  
Takagi Sugeno

In many practical systems, stochastic behaviors usually occur and need to be considered in the controller design. To ensure the system performance under the effect of stochastic behaviors, the controller may become bigger even beyond the capacity of practical applications. Therefore, the actuator saturation problem also must be considered in the controller design. The type-2 Takagi-Sugeno (T-S) fuzzy model can describe the parameter uncertainties more completely than the type-1 T-S fuzzy model for a class of nonlinear systems. A fuzzy controller design method is proposed in this paper based on the Interval Type-2 (IT2) T-S fuzzy model for stochastic nonlinear systems subject to actuator saturation. The stability analysis and some corresponding sufficient conditions for the IT2 T-S fuzzy model are developed using Lyapunov theory. Via transferring the stability and control problem into Linear Matrix Inequality (LMI) problem, the proposed fuzzy control problem can be solved by the convex optimization algorithm. Finally, a nonlinear ship steering system is considered in the simulations to verify the feasibility and efficiency of the proposed fuzzy controller design method.

Fuzzy Controller Design for Nonlinear Impulsive Fuzzy Systems With Time Delay

2011 ◽  
Vol 19 (5) ◽  
pp. 844-856 ◽  
Author(s):  
Hao Zhang ◽  
Huaicheng Yan ◽  
Tao Liu ◽  
Qijun Chen
Keyword(s):  
Time Delay ◽  
Fuzzy Systems ◽  
Fuzzy Controller ◽  
Controller Design

ANTICONTROL OF CHAOS FOR A CONTINUOUS-TIME TAKAGI–SUGENO FUZZY SYSTEM VIA LOCAL TIME-DELAY FEEDBACK

2005 ◽  
Vol 15 (12) ◽  
pp. 3883-3894 ◽  
Author(s):  
TAEK RYONG KIM ◽  
YOUNG HOON JOO ◽  
JIN BAE PARK ◽  
GUANRONG CHEN
Keyword(s):  
Time Delay ◽  
Discrete Time ◽  
Continuous Time ◽  
Fuzzy System ◽  
Closed Loop ◽  
Fuzzy Controller ◽  
Design Method ◽  
Fuzzy Model ◽  
Takagi Sugeno ◽  
Systematic Control

In this paper, a simple and systematic control design method is proposed for making a continuous-time Takagi–Sugeno (T–S) fuzzy system chaotic. The concept of parallel distributed compensation is employed to determine the structure of a fuzzy controller from a T–S fuzzy model. The fuzzy controller makes the T–S fuzzy model, which could be stable or unstable, bounded and chaotic. The verification of chaos in the closed-loop T–S fuzzy system is done by the following procedure. First, we establish an asymptotically approximate relationship between a continuous-time T–S fuzzy system with time-delay and a discrete-time T–S fuzzy system. Then, we verify the chaos in the closed-loop T–S fuzzy system by applying the Marotto theorem to its associated discrete-time T–S fuzzy system. The generated chaos is in the sense of Li and Yorke. Two examples are given to show that this methodology is simple and effective for anticontrol of chaos for a continuous-time T–S fuzzy system.

Fuzzy-Model-Based Adaptive Control for a Kind of Discrete-Time Systems with Time-Delay and Disturbances

2014 ◽  
Vol 22 (03) ◽  
pp. 453-468
Author(s):  
Yi-Min Li ◽  
Yuan-Yuan Li
Keyword(s):  
Adaptive Control ◽  
Stability Analysis ◽  
Time Delay ◽  
Discrete Time ◽  
Sufficient Conditions ◽  
Fuzzy Model ◽  
Linear Matrix ◽  
Parameter Uncertainties ◽  
Model Based ◽  
The Stability

This paper presents the stability analysis of discrete-time fuzzy-model-based adaptive control systems with time-delay, parameter uncertainties and external disturbance. To facilitate the stability analysis, the T-S fuzzy model is employed to represent the discretetime nonlinear system. A fuzzy observer is used to estimate the state of the fuzzy system, by using the estimations of states and nonlinear functions, and sufficient conditions for designing observer-based fuzzy controllers are proposed. The control and observer matrices involved can be determined by solving a set of linear matrix inequality (LMI). Finally, the numerical example carried out also demonstrate the feasibility of the design method.

A NOVEL DELAY-DEPENDENT CRITERION FOR TIME-DELAY T-S FUZZY SYSTEMS USING FUZZY LYAPUNOV METHOD

2007 ◽  
Vol 16 (03) ◽  
pp. 545-552 ◽  
Author(s):  
CHENG-WU CHEN ◽  
CHEN-LIANG LIN ◽  
CHUNG-HUNG TSAI ◽  
CHEN-YUAN CHEN ◽  
KEN YEH
Keyword(s):  
Time Delay ◽  
Lyapunov Functions ◽  
Fuzzy Systems ◽  
Lyapunov Method ◽  
Controller Design ◽  
Linear Matrix ◽  
Matrix Inequalities ◽  
Quadratic Lyapunov Functions ◽  
The Stability ◽  
Delay Dependent

This study presents an H∞ controller design for time-delay T-S fuzzy systems based on the fuzzy Lyapunov method, which is defined in terms of fuzzy blending quadratic Lyapunov functions. The delay-dependent robust stability criterion is derived in terms of the fuzzy Lyapunov method to guarantee the stability of time-delay T-S fuzzy systems subjected to disturbances. Based on the delay-dependent condition and parallel distributed compensation (PDC) scheme, the controller design problem is transformed into solving linear matrix inequalities (LMI).

AN OPTIMAL REAL-TIME TRAJECTORY TRACKING CONTROL DESIGN FOR PENDUBOT VIA TAKAGI-SUGENO FUZZY MODEL

2005 ◽  
Vol 29 (2) ◽  
pp. 247-265
Author(s):  
Zhen Cai ◽  
Chun-Yi Su
Keyword(s):  
Real Time ◽  
Trajectory Tracking ◽  
Fuzzy Controller ◽  
Fuzzy Model ◽  
Trajectory Tracking Control ◽  
Linear Regulator ◽  
Control Scheme ◽  
The Stability ◽  
Takagi Sugeno ◽  
Underactuated Robot

In this paper, an optimal fuzzy control scheme is presented to achieve trajectory tracking for the Pendubot, an underactuated robot by combining linear optimal control theory and linear regulator theory with the Takagi-Sugeno fuzzy methodology. The stability of the entire closed-loop fuzzy system is analyzed by the designed optimal fuzzy controller. The real-time application of the proposed algorithm on the Pendubot is also addressed.

Discrete Fuzzy Controller Design for Achieving Common State Covariance Assignment

2004 ◽  
Vol 126 (3) ◽  
pp. 627-632 ◽  
Author(s):  
Wen-Jer Chang ◽  
Chong-Cheng Shing
Keyword(s):  
Feedback Control ◽  
Covariance Matrix ◽  
Output Feedback ◽  
Fuzzy Systems ◽  
Fuzzy Controller ◽  
Controller Design ◽  
Fuzzy Model ◽  
Output Feedback Control ◽  
Control Approach ◽  
Takagi Sugeno

In this paper, a method is developed to find the output feedback fuzzy controllers for assigning a common state covariance matrix of discrete Takagi–Sugeno (T–S) fuzzy systems. The fuzzy control approach developed in this paper is based on the concept of Parallel Distributed Compensation (PDC). For each rule of the discrete T–S fuzzy model, it shows how to parameterize the static linear output feedback control gains to achieve a common state covariance matrix for each subsystem. Finally, a numerical example is provided to verify the effects of the proposed method.

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