Gain Margins in a Class of Nonlinear Systems: Lyapunov approach

2020 ◽  
Author(s):  
Siddharth Sankar Das ◽  
Yuri Shtessel ◽  
Franck Plestan ◽  
Shamila Nateghi ◽  
Rajesh R.J.
Keyword(s):  
Nonlinear Systems ◽  
Lyapunov Approach

A self-tuning PID control for a class of nonlinear systems based on the Lyapunov approach

2002 ◽  
Vol 12 (2) ◽  
pp. 233-242 ◽  
Author(s):  
Wei-Der Chang ◽  
Rey-Chue Hwang ◽  
Jer-Guang Hsieh
Keyword(s):  
Nonlinear Systems ◽  
Pid Control ◽  
Lyapunov Approach ◽  
Self Tuning

Event-triggered Neural Network-Based Adaptive Control for a Class of Uncertain Nonlinear Systems

2021 ◽  
pp. 2150275
Author(s):  
Hui Hu ◽  
Yang Li ◽  
Wei Yi ◽  
Yuebiao Wang ◽  
Fan Qu ◽  
...  
Keyword(s):  
Neural Network ◽  
Adaptive Control ◽  
Nonlinear Systems ◽  
Control Method ◽  
Uncertain Nonlinear Systems ◽  
Network Resource ◽  
Lyapunov Approach ◽  
Stability Method ◽  
The Stability ◽  
Event Triggered

In the paper, an event triggering adaptive control method based on neural network (NN) is proposed for a class of uncertain nonlinear systems with external disturbances. In order to reduce the network resource utilization, a novel event-triggered condition by the Lyapunov approach is proposed. In addition, the NN controller and adaptive parameters determined by the Lyapunov stability method are updated only at triggered instants to reduce the amount of calculation. Only one NN is used as the controller in the entire system. The stability analysis results of the closed-loop system are obtained by the Lyapunov approach, which shows that all the signals in the systems with bounded disturbance are semi-globally bounded. Zeno behavior is avoided. Finally, the analytical design is confirmed by the simulation results on a two-link robotic manipulator.

scholarly journals Adaptive Second-Order Sliding Mode Control Design for a Class of Nonlinear Systems with Unknown Input

2015 ◽  
Vol 2015 ◽  
pp. 1-7 ◽  
Author(s):  
You Zheng ◽  
Jianxing Liu ◽  
Xinyi Liu ◽  
Dandan Fang ◽  
L. Wu
Keyword(s):  
Nonlinear Systems ◽  
Sliding Mode ◽  
Time Derivative ◽  
Second Order ◽  
Sliding Mode Controller ◽  
Unknown Input ◽  
Time Stability ◽  
Lyapunov Approach ◽  
Finite Time Stability ◽  
Second Order Sliding Mode

An adaptive second-order sliding mode controller is proposed for a class of nonlinear systems with unknown input. The proposed controller continuously drives the sliding variable and its time derivative to zero in the presence of disturbances withunknownboundaries. A Lyapunov approach is used to show finite time stability for the system in the presence of a class of uncertainty. An illustrative simulation example is presented to demonstrate the performance and robustness of the proposed controller.

DISCRETE-TIME REDUCED ORDER NEURAL OBSERVERS FOR UNCERTAIN NONLINEAR SYSTEMS

2010 ◽  
Vol 20 (01) ◽  
pp. 29-38 ◽  
Author(s):  
ALMA Y. ALANIS ◽  
EDGAR N. SANCHEZ ◽  
LUIS J. RICALDE
Keyword(s):  
Nonlinear Systems ◽  
Discrete Time ◽  
Nonlinear Oscillator ◽  
Estimation Error ◽  
Reduced Order ◽  
Lyapunov Approach ◽  
Simulation Results ◽  
Parallel Configuration ◽  
The Stability ◽  
High Order Neural Network

This paper focusses on a novel discrete-time reduced order neural observer for nonlinear systems, which model is assumed to be unknown. This neural observer is robust in presence of external and internal uncertainties. The proposed scheme is based on a discrete-time recurrent high order neural network (RHONN) trained with an extended Kalman filter (EKF)-based algorithm, using a parallel configuration. This work includes the stability proof of the estimation error on the basis of the Lyapunov approach; to illustrate the applicability, simulation results for a nonlinear oscillator are included.

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