Synthesis of variable gain controllers for a class of uncertain nonlinear systems via piecewise Lyapunov functions

2009 ◽  
Author(s):  
Hidetoshi Oya ◽  
Kojiro Hagino
Keyword(s):  
Nonlinear Systems ◽  
Lyapunov Functions ◽  
Uncertain Nonlinear Systems ◽  
Variable Gain

scholarly journals ℒ2Gain Performance for a Class of Lipschitz Uncertain Nonlinear Systems via Variable Gain Robust Output Feedback Controllers

2013 ◽  
Vol 2013 ◽  
pp. 1-7 ◽  
Author(s):  
Hidetoshi Oya ◽  
Kojiro Hagino
Keyword(s):  
Nonlinear Systems ◽  
Output Feedback ◽  
Sufficient Conditions ◽  
Feedback Controller ◽  
Linear Matrix ◽  
Uncertain Nonlinear Systems ◽  
Matrix Inequalities ◽  
Feedback Controllers ◽  
Variable Gain ◽  
Output Feedback Controller

We consider a design problem of a variable gain robust output feedback controller with guaranteedℒ2gain performance for a class of Lipschitz uncertain nonlinear systems. The proposed variable gain robust output feedback controller achieves not only robust stability but also a specifiedℒ2gain performance. In this paper, we show that sufficient conditions for the existence of the proposed variable gain robust output feedback controller with guaranteedℒ2gain performance are given in terms of linear matrix inequalities (LMIs). Finally, a simple numerical example is included.

Semi-Global Adaptive Control Using Barrier Lyapunov Functions for a Class of Uncertain Nonlinear Systems

2012 ◽  
Vol 190-191 ◽  
pp. 1053-1056
Author(s):  
Peng Nian Chen ◽  
Ling Ling Sun
Keyword(s):  
Neural Networks ◽  
Adaptive Control ◽  
Nonlinear Systems ◽  
Lyapunov Functions ◽  
Closed Loop ◽  
Control Method ◽  
Uncertain Nonlinear Systems ◽  
Closed Loop System ◽  
Periodic Disturbances ◽  
Barrier Lyapunov Functions

This paper deals with the problem of adaptive control for a class of nonlinear systems. The system considered in the paper contains non-parameterized uncertainties and periodic disturbances. A semi-global adaptive control method is proposed using the barrier Lyapunov functions. The method does not need to assume that the solution of the closed loop system remains in the domain of approximation by neural networks and guarantees that the solution of the system converges to zero.

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