On-line neural training algorithm with sliding mode control and adaptive learning rate

2007 ◽  
Vol 70 (16-18) ◽  
pp. 2687-2691 ◽  
Author(s):  
A. Nied ◽  
S.I. Seleme ◽  
G.G. Parma ◽  
B.R. Menezes
Keyword(s):  
Sliding Mode Control ◽  
Adaptive Learning ◽  
Sliding Mode ◽  
Learning Rate ◽  
Training Algorithm ◽  
Mode Control ◽  
Adaptive Learning Rate ◽  
On Line

Adaptive Sliding Mode Control of MEMS Vibrational Gyroscope

2006 ◽  
Author(s):  
J. Fei ◽  
C. Batur
Keyword(s):  
Sliding Mode Control ◽  
Sliding Mode ◽  
Angular Rate ◽  
External Disturbance ◽  
Adaptive Sliding Mode Control ◽  
Angular Velocity Vector ◽  
Adaptive Sliding Mode ◽  
Mode Control ◽  
Control Scheme ◽  
On Line

This paper presents a new sliding mode adaptive controller for MEMS z-axis gyroscope. The proposed adaptive sliding mode control algorithm can on-line estimate the component of the angular velocity vector, which is orthogonal to the plane of oscillation of the gyroscope (the z-axis) and the linear damping and stiffness model coefficients. The stability of the closed-loop system can be guaranteed with the proposed control strategy. The numerical simulation for MEMS Gyroscope is investigated to verify the effectiveness of the proposed adaptive sliding mode control scheme. It is shown that the proposed adaptive sliding mode control scheme offers several advantages such as on-line estimation of gyroscope parameters including angular rate and large robustness to parameter variations and external disturbance.

scholarly journals Stability Analysis of Neural Networks-Based System Identification

2008 ◽  
Vol 2008 ◽  
pp. 1-8 ◽  
Author(s):  
Talel Korkobi ◽  
Mohamed Djemel ◽  
Mohamed Chtourou
Keyword(s):  
Stability Analysis ◽  
Adaptive Learning ◽  
Learning Rule ◽  
Stability Domain ◽  
Learning Rate ◽  
Training Algorithm ◽  
Adaptive Learning Rate ◽  
Systems Identification ◽  
The Stability ◽  
Convergence Stability

This paper treats some problems related to nonlinear systems identification. A stability analysis neural network model for identifying nonlinear dynamic systems is presented. A constrained adaptive stable backpropagation updating law is presented and used in the proposed identification approach. The proposed backpropagation training algorithm is modified to obtain an adaptive learning rate guarantying convergence stability. The proposed learning rule is the backpropagation algorithm under the condition that the learning rate belongs to a specified range defining the stability domain. Satisfying such condition, unstable phenomena during the learning process are avoided. A Lyapunov analysis leads to the computation of the expression of a convenient adaptive learning rate verifying the convergence stability criteria. Finally, the elaborated training algorithm is applied in several simulations. The results confirm the effectiveness of the CSBP algorithm.

Sign in / Sign up

Export Citation Format

Share Document