Robust neuro control for rotating disc vibrations

2000 ◽  
Vol 214 (12) ◽  
pp. 1529-1544 ◽  
Author(s):  
C-L Lin ◽  
V-T Liu ◽  
P-S Hao
Keyword(s):  
Neural Network ◽  
Vibration Suppression ◽  
Spillover Effect ◽  
Rotating Disc ◽  
Robust Controller ◽  
Neural Network Learning ◽  
Control Scheme ◽  
Adaptive Adjustment ◽  
Multilayer Feedforward Neural Network ◽  
Fast Vibration

A novel robust-neuro control scheme for rotating disc vibrations is explored. In the approach proposed, a robust controller is first adopted to suppress the regulation error resulting from the unmodelled residuals while avoiding the spillover effect. In addition to the robust controller, a multilayer feedforward neural network is introduced to improve regulation performance. To maintain loop stability during neural network learning, a confinement algorithm for adaptive adjustment of the network weights is investigated. Simulation studies for a flexible rotating disc show that the control scheme proposed provides fast vibration suppression while maintaining robust stability.

Travelling Wave Control of Rotating Discs and Analysis of its Spillover Effect

1988 ◽  
Vol 202 (2) ◽  
pp. 119-127 ◽  
Author(s):  
C-S Kim ◽  
C-W Lee
Keyword(s):  
Travelling Waves ◽  
Spillover Effect ◽  
Polynomial Equation ◽  
Travelling Wave ◽  
Rotating Disc ◽  
System A ◽  
Finite Dimensional ◽  
Control Scheme ◽  
Actuator System ◽  
Wave Control

A modal control scheme for rotating disc systems is developed based upon the finite-dimensional sub-system model including a few lower backward travelling waves important to the disc response. For the single discrete sensor and actuator system, a polynomial equation, which determines the closed-loop system poles, is derived and the spillover effect is analysed, providing a sufficient condition for stability. Finally, simulation studies are performed to show the effectiveness of the travelling wave control scheme proposed.

NEURAL ADAPTIVE CONTROL FOR VIBRATION SUPPRESSION IN COMPOSITE FIN-TIP OF AIRCRAFT

2008 ◽  
Vol 18 (03) ◽  
pp. 219-231 ◽  
Author(s):  
S. SURESH ◽  
N. KANNAN ◽  
N. SUNDARARAJAN ◽  
P. SARATCHANDRAN
Keyword(s):  
Neural Network ◽  
Finite Element ◽  
Adaptive Control ◽  
Inverse Dynamics ◽  
Vibration Suppression ◽  
Mode Shapes ◽  
Neural Adaptive Control ◽  
Control Scheme ◽  
Adaptive Neural Network Control ◽  
Adaptive Control Scheme

In this paper, we present a neural adaptive control scheme for active vibration suppression of a composite aircraft fin tip. The mathematical model of a composite aircraft fin tip is derived using the finite element approach. The finite element model is updated experimentally to reflect the natural frequencies and mode shapes very accurately. Piezo-electric actuators and sensors are placed at optimal locations such that the vibration suppression is a maximum. Model-reference direct adaptive neural network control scheme is proposed to force the vibration level within the minimum acceptable limit. In this scheme, Gaussian neural network with linear filters is used to approximate the inverse dynamics of the system and the parameters of the neural controller are estimated using Lyapunov based update law. In order to reduce the computational burden, which is critical for real-time applications, the number of hidden neurons is also estimated in the proposed scheme. The global asymptotic stability of the overall system is ensured using the principles of Lyapunov approach. Simulation studies are carried-out using sinusoidal force functions of varying frequency. Experimental results show that the proposed neural adaptive control scheme is capable of providing significant vibration suppression in the multiple bending modes of interest. The performance of the proposed scheme is better than the H∞ control scheme.

Neural Network-Based Speed Control of A Two-Mass-Model System

1999 ◽  
Vol 3 (5) ◽  
pp. 427-430 ◽  
Author(s):  
Rached Dhaouadi ◽  
◽  
Khaled Nouri
Keyword(s):  
Neural Network ◽  
Inverse Dynamics ◽  
Mass Model ◽  
Neural Network Learning ◽  
Inverse Control ◽  
Network Learning ◽  
The Neural Network ◽  
Control Scheme ◽  
On Line ◽  
Neural Network Structure

We present an application of artificial neural networks to the problem of controlling the speed of an elastic drive system. We derive a neural network structure to simulate the inverse dynamics of the system, then implement the direct inverse control scheme in a closed loop. The neural network learning is done on-line to adaptively control the speed to follow a stepwise changing reference. The experimental results with a two-mass-model analog board confirm the effectiveness of the proposed neurocontrol scheme.

Tracking Control of Robot Manipulator Based on Robust Neural Network Control

2011 ◽  
Vol 308-310 ◽  
pp. 1238-1241
Author(s):  
San Xiu Wang ◽  
Guang Ying Yang
Keyword(s):  
Neural Network ◽  
Upper Bound ◽  
Tracking Control ◽  
Robot Manipulator ◽  
Network Control ◽  
Neural Network Control ◽  
Robust Controller ◽  
Control Scheme

This paper presents a robust neural network control scheme for robot manipulator. The robust controller is employed to eliminate the effect of the uncertainties, and a neural network is utilized to learn the unknown uncertain upper bound, which improves the performance. The experiments have been implemented and demonstrate the validation of the proposed method.

Speed up Neural Network Learning by GPGPU

2011 ◽  
Vol 131 (11) ◽  
pp. 1889-1894
Author(s):  
Yuta Tsuchida ◽  
Michifumi Yoshioka
Keyword(s):  
Neural Network ◽  
Neural Network Learning ◽  
Network Learning ◽  
Speed Up
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