A FUZZY NEURAL NETWORK FOR THE ACTIVE VIBRATION CONTROL OF A CENTRIFUGAL PENDULUM VIBRATION ABSORBER

2009 ◽  
Vol 20 (12) ◽  
pp. 1963-1979
Author(s):  
CHI-HSIUNG LIANG ◽  
PI-CHENG TUNG
Keyword(s):  
Neural Network ◽  
Bp Neural Network ◽  
Active Vibration Control ◽  
Bp Algorithm ◽  
Vibration Absorber ◽  
Neural Network Controller ◽  
Network Controller ◽  
Centrifugal Pendulum Vibration Absorber ◽  
Active Vibration ◽  
Simulation Results

In this study, we develop a fuzzy back-propagation (BP) neural network controller for active vibration control of a centrifugal pendulum vibration absorber (CPVA). The fuzzy BP neural network controller systems can be viewed as a conventional fuzzy algorithm for coarse tuning. The BP algorithm can also be applied for fine tuning, in this case to regulate the anti-resonance frequency in an active pendulum vibration absorber (APVA), by suppressing vibration of the carrier. The dynamic model of the APVA was developed and simulated using MATLAB. In the simulation results, when the frequency of the disturbance changes, the outputs of the fuzzy BP neural network controller are used to determine an appropriate value for the torque of the active pendulum such that the vibration amplitude of the carrier is minimized. A comparison of the carrier vibration results for the CPVA, the fuzzy algorithm and the fuzzy BP algorithm is performed. The simulation results demonstrate the effectiveness of the proposed fuzzy BP neural network APVA for reducing the carrier vibrations.

scholarly journals Active Vibration Control of the Smart Plate Using Artificial Neural Network Controller

2015 ◽  
Vol 2015 ◽  
pp. 1-20 ◽  
Author(s):  
Mohit ◽  
Deepak Chhabra ◽  
Suresh Kumar
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Vibration Control ◽  
Active Vibration Control ◽  
Cantilever Plate ◽  
Neural Network Controller ◽  
The Neural Network ◽  
Network Controller ◽  
Active Vibration ◽  
Artificial Neural

The active vibration control (AVC) of a rectangular plate with single input and single output approach is investigated using artificial neural network. The cantilever plate of finite length, breadth, and thickness having piezoelectric patches as sensors/actuators fixed at the upper and lower surface of the metal plate is considered for examination. The finite element model of the cantilever plate is utilized to formulate the whole strategy. The compact RIO and MATLAB simulation software are exercised to get the appropriate results. The cantilever plate is subjected to impulse input and uniform white noise disturbance. The neural network is trained offline and tuned with LQR controller. The various training algorithms to tune the neural network are exercised. The best efficient algorithm is finally considered to tune the neural network controller designed for active vibration control of the smart plate.

The Application of Double Closed-Loop Unit Gain Negative Feedback and BP Neural Network Controller in Single Inverted Pendulum

2012 ◽  
Vol 490-495 ◽  
pp. 1723-1727
Author(s):  
Jun Ting Wang ◽  
Guo Ping Liu ◽  
Wei Jin ◽  
Gen Fu Xiao
Keyword(s):  
Neural Network ◽  
Mathematical Model ◽  
Negative Feedback ◽  
Bp Neural Network ◽  
Closed Loop ◽  
Inverted Pendulum ◽  
Root Locus ◽  
Neural Network Controller ◽  
Network Controller ◽  
The Mathematical Model

In the paper the mathematical model of the single inverted pendulum is established, on the base of the root locus and the control tasks the control system is made up of double closed-loop unit gain negative feedback and BP neural network controller. The results show that the inverted pendulum is efficiently controlled.

Application of BP Neural Network Controller on Inverted Pendulum

2013 ◽  
Vol 328 ◽  
pp. 72-76
Author(s):  
Huan Xin Cheng ◽  
Dao Sheng Zhang ◽  
Li Cheng
Keyword(s):  
Neural Network ◽  
Bp Neural Network ◽  
Inverted Pendulum ◽  
Network Control ◽  
Coupled Systems ◽  
Control Effect ◽  
Strongly Coupled ◽  
Neural Network Controller ◽  
Network Controller ◽  
The Mathematical Model

The traditional PID control, which is based on linearization, is often hard to obtain the optimal control effect on such nonlinear, multiple-output, strongly coupled systems like inverted pendulum. To solve the problem above, the BP neural network controller was developed for inverted pendulum. On the basis of establishing and analyzing the mathematical model of single inverted-pendulum, this paper established the state space expression, and then designed a neural network control system based on BP algorithm. The simulation was researched by Matlab and the running results show that this control has good robustness and can achieve satisfactory control effect.

Character Image Recognition Based on the Improved BP Neural Network

2011 ◽  
Vol 58-60 ◽  
pp. 2655-2658 ◽  
Author(s):  
Hong Zhao
Keyword(s):  
Neural Network ◽  
Computer Simulation ◽  
Image Recognition ◽  
Bp Neural Network ◽  
Bp Algorithm ◽  
Simulation Results ◽  
Improved Bp Algorithm

This paper raises a kind of improved BP algorithm in order to compensate for some shortcomings which exist in traditional BP neural network. It has been applied to the recognition of character images. Computer simulation results demonstrate that it does bring about an ideal result.

Active Vibration Control for Lathe Based Neural Network

2011 ◽  
Vol 84-85 ◽  
pp. 183-187 ◽  
Author(s):  
Jin Hua Wang ◽  
Wen Juan Huang ◽  
Hong Yan Zhang ◽  
Yao Gang Li
Keyword(s):  
Neural Network ◽  
Control System ◽  
Vibration Control ◽  
Active Vibration Control ◽  
System Model ◽  
Matlab Simulation ◽  
Model Based ◽  
Active Vibration ◽  
Simulation Results ◽  
Control System Model

In this paper, we took lathe as the research object, and established an active vibration control system model based on neural network AVC (Active Vibration Control) system, and the Matlab simulation results showed that the AVC system can reduce vibration effectively and improve the lathe’s accuracy.

Recognition of Paper Currency Research Based on AGA-BP Neural Network

2014 ◽  
Vol 989-994 ◽  
pp. 3968-3972
Author(s):  
Xue Xiao ◽  
Qing Hong Wu ◽  
Ying Zhang
Keyword(s):  
Neural Network ◽  
Genetic Algorithm ◽  
Bp Neural Network ◽  
Biological Evolution ◽  
Adaptive Genetic Algorithm ◽  
Neural Network Controller ◽  
Network Controller ◽  
Paper Currency ◽  
Randomized Search ◽  
Banknote Recognition

The genetic algorithm is a randomized search method for a class of reference biological evolution of the law evolved, with global implicit parallelism inherent and better optimization. This paper presents an adaptive genetic algorithm to optimize the use of BP neural network method, namely the structure of weights and thresholds to optimize BP neural network to achieve the recognition of banknotes oriented. Experimental results show that after using genetic algorithms to optimize BP neural network controller can accurately and quickly achieved recognition effect on banknote recognition accuracy compared to traditional BP neural network has been greatly improved, improved network adaptive capacity and generalization ability.

A Combination of Fuzzy Logic and Neural Network Algorithms for Active Vibration Control

1996 ◽  
Vol 210 (3) ◽  
pp. 153-167 ◽  
Author(s):  
S-J Huang ◽  
R-J Lian
Keyword(s):  
Neural Network ◽  
Fuzzy Logic ◽  
Vibration Control ◽  
Vibration Amplitude ◽  
Mimo System ◽  
Active Vibration Control ◽  
Lumped Mass ◽  
Network Algorithms ◽  
Neural Network Controller ◽  
Active Vibration

The construction of a dynamic absorber incorporating active vibration control is described. The absorber is a 2 degree of freedom spring-lumped mass system sliding on a guide pillar, with two internal vibration disturbance sources. Both the main mass and the secondary absorber mass were acted on by direct current (d.c.) servo motors, respectively, to suppress the vibration amplitude. In this paper, a new control approach is proposed by combining fuzzy logic and neural network algorithms to control the multi-input/multi-output (MIMO) system. Firstly, the fuzzy logic controller was designed for controlling the main influence part of the MIMO system. Secondly, the coupling neural network controller was employed to take care of the coupling effect and refine the control performance of the MIMO system. The experimental results show that the control system effectively suppresses the vibration amplitude and with good position tracking accuracy.

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