Asynchronous dissipative filter design of nonhomogeneous Markovian jump fuzzy systems via relaxation of triple-parameterized matrix inequalities

2019 ◽  
Vol 478 ◽  
pp. 564-579 ◽  
Author(s):  
Sung Hyun Kim
Keyword(s):  
Fuzzy Systems ◽  
Filter Design ◽  
Matrix Inequalities ◽  
Markovian Jump

scholarly journals Robust Quantized GeneralizedH2Filtering for Uncertain Discrete-Time Fuzzy Systems

2015 ◽  
Vol 2015 ◽  
pp. 1-11
Author(s):  
Jidong Wang ◽  
Xiaoping Si ◽  
Kezhen Han
Keyword(s):  
Discrete Time ◽  
Fuzzy Systems ◽  
Filter Design ◽  
Sufficient Conditions ◽  
Linear Matrix ◽  
Noise Attenuation ◽  
Matrix Inequalities ◽  
Slack Variables ◽  
Fuzzy Lyapunov Function ◽  
Attenuation Level

This paper deals with the problem of robust generalizedH2filter design for uncertain discrete-time fuzzy systems with output quantization. Firstly, the outputs of the system are quantized by a memoryless logarithmic quantizer before being transmitted to a filter. Then, attention is focused on the design of a generalizedH2filter to mitigate quantization effects, such that the filtering error systems ensure the robust stability with a prescribed generalizedH2noise attenuation level. Via applying Finsler lemma to introduce some slack variables and using the fuzzy Lyapunov function, sufficient conditions for the existence of a robust generalizedH2filter are expressed in terms of linear matrix inequalities (LMIs). Finally, a numerical example is provided to demonstrate the effectiveness of the proposed approach.

Dissipativity analysis of delayed stochastic generalized neural networks with Markovian jump parameters

2021 ◽  
Vol 0 (0) ◽  
Author(s):  
Grienggrai Rajchakit ◽  
Ramalingam Sriraman ◽  
Rajendran Samidurai
Keyword(s):  
Linear Matrix ◽  
Time Varying ◽  
Matrix Inequalities ◽  
Markovian Jump ◽  
Stochastic Perturbations ◽  
Practical Applications ◽  
Markovian Jump Parameters ◽  
Generalized Neural Networks ◽  
System Information ◽  
Dissipativity Analysis

Abstract This article discusses the dissipativity analysis of stochastic generalized neural network (NN) models with Markovian jump parameters and time-varying delays. In practical applications, most of the systems are subject to stochastic perturbations. As such, this study takes a class of stochastic NN models into account. To undertake this problem, we first construct an appropriate Lyapunov–Krasovskii functional with more system information. Then, by employing effective integral inequalities, we derive several dissipativity and stability criteria in the form of linear matrix inequalities that can be checked by the MATLAB LMI toolbox. Finally, we also present numerical examples to validate the usefulness of the results.

scholarly journals New approach to finite frequency Filter Design for two-dimensional T-S fuzzy systems

2021 ◽  
Vol 297 ◽  
pp. 01036
Author(s):  
Ben Meziane Khaddouj ◽  
Abderrahim El-Amrani ◽  
Ismail Boumhidi
Keyword(s):  
Fuzzy Systems ◽  
Filter Design ◽  
Asymptotically Stable ◽  
Two Dimensional ◽  
Finite Frequency ◽  
New Approach ◽  
Filter Model ◽  
Error System ◽  
Non Linear Systems ◽  
Takagi Sugeno

This paper considers the problem of filter design for two-dimensional (2D) discrete-time non-linear systems in Takagi-Sugeno (T-S) fuzzy mode. The problem to be solved in the paper is to find a H∞ filter model such that the filtering error system is asymptotically stable. A numerical example is employed to illustrate the validity of the proposed methods.

scholarly journals Robust Stabilization of Stochastic Markovian Jump Systems with Distributed Delays

Complexity ◽  
2021 ◽  
Vol 2021 ◽  
pp. 1-8
Author(s):  
Guilei Chen ◽  
Zhenwei Zhang ◽  
Chao Li ◽  
Dianju Qiao ◽  
Bo Sun
Keyword(s):  
Linear Matrix Inequalities ◽  
Robust Stabilization ◽  
Distributed Delays ◽  
Markovian Jump Systems ◽  
Linear Matrix ◽  
Matrix Inequalities ◽  
Markovian Jump ◽  
Parameter Uncertainties ◽  
Delay Dependent ◽  
Jump Systems

This paper addresses the robust stabilization problem for a class of stochastic Markovian jump systems with distributed delays. The systems under consideration involve Brownian motion, Markov chains, distributed delays, and parameter uncertainties. By an appropriate Lyapunov–Krasovskii functional, the novel delay-dependent stabilization criterion for the stochastic Markovian jump systems is derived in terms of linear matrix inequalities. When given linear matrix inequalities are feasible, an explicit expression of the desired state feedback controller is given. The designed controller, based on the obtained criterion, ensures asymptotically stable in the mean square sense of the resulting closed-loop system. The convenience of the design is greatly enhanced due to the existence of an adjustable parameter in the controller. Finally, a numerical example is exploited to demonstrate the effectiveness of the developed theory.

Simultaneous Fault Detection and Control for Continuous-Time Switched T-S Fuzzy Systems with State Jumps

2021 ◽  
Author(s):  
Ayyoub Ait Ladel ◽  
Abdellah Benzaouia ◽  
Rachid Outbib ◽  
Mustapha Ouladsine
Keyword(s):  
Fault Detection ◽  
Fuzzy Systems ◽  
Degrees Of Freedom ◽  
Average Dwell Time ◽  
Single Step ◽  
Linear Matrix ◽  
Matrix Inequalities ◽  
Robust Detection ◽  
And Control ◽  
Detector Design

Abstract This paper addresses the simultaneous fault detection and control (SFDC) issue for switched T-S fuzzy systems with state jumps. The main objective is to design robust detection filters and observer-based controllers to stabilize this system class and, at the same time, detect the presence of faults. Less conservative stability conditions are developed, applying the mode-dependent average dwell time (MDADT) concept, the robust H_{\infty} approach, and the piecewise Lyapunov function (PLF) technique. Based on these conditions, the integrated controller and detector design is formalized in the form of linear matrix inequalities (LMI) instead of bilinear matrix inequalities (BMI). The proposed LMIs determine the controller/ detector gains simultaneously in a single step, thus offering more degrees of freedom in the design. Finally, a numerical example and two real systems examples are studied to prove the applicability and effectiveness of the obtained results.

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