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.

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.

Application of Multivariable Non-Linear Decoupling Control Based on Neural Network

2012 ◽  
Vol 214 ◽  
pp. 786-791
Author(s):  
Jian Bo Zhang ◽  
Dong Hai Fan ◽  
Ren Zhi Hu
Keyword(s):  
Neural Network ◽  
Nonlinear System ◽  
Distributed Control ◽  
Decoupling Control ◽  
Distributed Control System ◽  
Neural Network Controller ◽  
Mimo Nonlinear System ◽  
The Neural Network ◽  
Network Controller ◽  
The Mathematical Model

Aimed at Neural Network can approach any nonlinear system with arbitrary accuracy, the frame of distributed NN decoupling system are proposed to decouple the MIMO nonlinear system. In this paper, we designed and finished the Distributed Control System based on ABB’s Freelance 800F, and collected experimental data to model the thermostatic heater, then we have carried out the mathematical model by means of MATLAB dynamic simulation. In sequence, we trained the neural network controller in MATLAB. When the decoupling is completed, we used controller to control the MIMO nonlinear system in DCS. Experiment result shows that it is conscientiously feasible and deserves to be widely applied in the process of controlling industry.

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.

A Neural Network Approach for Milling Process Control

2004 ◽  
Vol 471-472 ◽  
pp. 107-111 ◽  
Author(s):  
Z. Yang ◽  
T. Huang ◽  
Y.M. Yang
Keyword(s):  
Neural Network ◽  
Bp Neural Network ◽  
Fuzzy Logic Control ◽  
Milling Process ◽  
Gradual Change ◽  
Neural Network Approach ◽  
Logic Control ◽  
Neural Network Controller ◽  
The Neural Network ◽  
Network Controller

One of key approaches to improve the productivity is to control with constant force in the milling process by adjusting the feed rate. In order to overcome the mismatch model occurred in adaptive control and inaccurate deducing regulation in fuzzy logic control, a three-layer BP neural network is designed for tracing reference force. First of all, control arithmetic is given, and a series of simulation work is achieved to determine the study factor. At last, aimed at two working conditions with abrupt and gradual change of cutting depth, the correctness and effectiveness of the neural network controller are proved by experiments.

Sign in / Sign up

Export Citation Format

Share Document