scholarly journals A linear matrix inequality (LMI) approach to robust H/sub 2/ sampled-data control for linear uncertain systems

2003 ◽  
Vol 33 (1) ◽  
pp. 149-155 ◽  
Author(s):  
Li-Sheng Hu ◽  
J. Lam ◽  
Yong-Yan Cao ◽  
Hui-He Shao
Keyword(s):  
Linear Matrix Inequality ◽  
Uncertain Systems ◽  
Linear Matrix ◽  
Matrix Inequality ◽  
Sampled Data ◽  
Lmi Approach ◽  
Data Control ◽  
Sampled Data Control

Differential linear matrix inequality in optimal sampled-data control

Automatica ◽  
2019 ◽  
Vol 100 ◽  
pp. 289-298 ◽  
Author(s):  
José C. Geromel ◽  
Patrizio Colaneri ◽  
Paolo Bolzern
Keyword(s):  
Linear Matrix Inequality ◽  
Linear Matrix ◽  
Matrix Inequality ◽  
Sampled Data ◽  
Data Control ◽  
Sampled Data Control

Exponentially Stable Sampled-Data Control for Uncertain Systems

2014 ◽  
Vol 981 ◽  
pp. 551-554
Author(s):  
Li Ying Fan
Keyword(s):  
Uncertain Systems ◽  
Sufficient Conditions ◽  
Linear Matrix ◽  
Control Input ◽  
Sampled Data ◽  
Data Control ◽  
Sampled Data Control ◽  
Exponentially Stable ◽  
Data Controller ◽  
Continuous Time System

In this paper, the problem of the exponentially stable sampled-data control was investigated for a class of uncertain systems. Based on the input delay approach, the system was modeled as a continuous-time system with the delayed control input. Attention was focused on the design of a state feedback sampled-data controller which guarantees the exponential stability of the closed-loop system for all admissible parametric uncertainties. Using linear matrix inequality (LMI) approach, sufficient conditions are obtained. Simulation example was given to demonstrate the effectiveness and correctness of the proposed method.

scholarly journals A New Robust Training Law for Dynamic Neural Networks with External Disturbance: An LMI Approach

2010 ◽  
Vol 2010 ◽  
pp. 1-14 ◽  
Author(s):  
Choon Ki Ahn
Keyword(s):  
Neural Networks ◽  
Linear Matrix Inequality ◽  
Exponential Stability ◽  
External Disturbance ◽  
Linear Matrix ◽  
Matrix Inequality ◽  
Input Output ◽  
Lmi Approach ◽  
Dynamic Neural Networks ◽  
Numerical Examples

A new robust training law, which is called an input/output-to-state stable training law (IOSSTL), is proposed for dynamic neural networks with external disturbance. Based on linear matrix inequality (LMI) formulation, the IOSSTL is presented to not only guarantee exponential stability but also reduce the effect of an external disturbance. It is shown that the IOSSTL can be obtained by solving the LMI, which can be easily facilitated by using some standard numerical packages. Numerical examples are presented to demonstrate the validity of the proposed IOSSTL.

Dissipativity-Based Reliable Sampled-Data Control With Nonlinear Actuator Faults

2016 ◽  
Vol 11 (6) ◽  
Author(s):  
Srimanta Santra ◽  
R. Sakthivel ◽  
B. Kaviarasan
Keyword(s):  
Sufficient Conditions ◽  
Control Design ◽  
Delay Systems ◽  
Linear Matrix ◽  
Optimization Approach ◽  
Actuator Faults ◽  
Sampled Data ◽  
Data Control ◽  
Sampled Data Control ◽  
Data Controller

In this paper, the problem of reliable sampled-data control design with strict dissipativity for a class of linear continuous-time-delay systems against nonlinear actuator faults is studied. The main objective of this paper is to design a reliable sampled-data controller to ensure a strictly dissipative performance for the closed-loop system. Based on the linear matrix inequality (LMI) optimization approach and Wirtinger-based integral inequality, a new set of sufficient conditions is established for reliable dissipativity analysis of the considered system by assuming the mixed actuator fault matrix to be known. Then, the proposed result is extended to unknown fault matrix case. Also, the reliable sampled-data controller with strict dissipativity is designed by solving a convex optimization problem which can be easily solved by using standard numerical algorithms. Finally, a numerical example based on liquid propellant rocket motor with a pressure feeding system model is presented to illustrate the effectiveness of the developed control design technique.

H∞ Control of Fuzzy Neutral Systems under Sampled-Data Control

2010 ◽  
Vol 108-111 ◽  
pp. 482-487
Author(s):  
Jun Yang ◽  
Wen Pin Luo ◽  
Gui Hua Li
Keyword(s):  
State Feedback ◽  
Design Method ◽  
Descriptor System ◽  
Sampled Data ◽  
Neutral Systems ◽  
Lmi Approach ◽  
Data Control ◽  
System Method ◽  
Sampled Data Control ◽  
Barbalat Lemma

This paper is concerned with the H∞ sampled-data control for a class of fuzzy neutral systems. Employing Lyapunov-Krasovskii functional, the input delay approach, the descriptor system method, Barbalat lemma and the LMI approach, a design method of sampled-data state feedback controller for the fuzzy neutral systems is proposed.

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