Self-Learning Control of PMA-Actuated Knee-Joint Rehabilitation Training Device Based on Fuzzy Neural Network

2011 ◽  
Vol 467-469 ◽  
pp. 1645-1650
Author(s):  
Xiao Li ◽  
Xia Hong ◽  
Ting Guan
Keyword(s):  
Neural Network ◽  
Knee Joint ◽  
Pid Controller ◽  
Fuzzy Neural Network ◽  
Control Method ◽  
Learning Control ◽  
Training Device ◽  
Rehabilitation Training ◽  
Fuzzy Neural ◽  
Self Learning

To solve the problem of the delay, nonlinearity and time-varying properties of PMA-actuated knee-joint rehabilitation training device, a self-learning control method based on fuzzy neural network is proposed in this paper. A self-learning controller was designed based on the combination of pid controller, feedforward controller, fuzzy neural network controller, and learning mechanism. It was applied to the isokinetic continuous passive motion control of the PMA-actuated knee-joint rehabilitation training device. The experiments proved that the self-learning controller has the properties of high control accuracy and unti-disturbance capability, comparing with pid controller. This control method provides the beneficial reference for improving the control performance of such system.

Self-Learning Control of Hydraulic Injection Molding Machine Based on Fuzzy Neural Network

2011 ◽  
Vol 66-68 ◽  
pp. 1117-1121
Author(s):  
Xiao Li ◽  
Xiao Hua Yang
Keyword(s):  
Neural Network ◽  
Injection Molding ◽  
Pid Controller ◽  
High Speed ◽  
Fuzzy Neural Network ◽  
Learning Control ◽  
Molding Machine ◽  
Injection Molding Machine ◽  
Fuzzy Neural ◽  
Self Learning

Considering the nonlinear and time-variable characteristics of the injection cylinder system of hydraulic injection molding machine (HIMM), a self-learning control method based on fuzzy neural network is proposed in this paper. A self-learning controller was designed based on the combination of PID controller, fuzzy neural network controller, and learning mechanism. It was applied to the position control of injection cylinder. The experimental results show that the controller has the property of higher position tracking accuracy under the high speed and variable track movement of injection cylinder, compared with PID controller. The conclusion of this research can provide the beneficial reference for designing high speed and high precision HIMM.

scholarly journals Single-Phase Photovoltaic Grid-Connected Inverter Based on Fuzzy Neural Network

2021 ◽  
Vol 25 (3) ◽  
pp. 310-316
Author(s):  
Shenping Xiao ◽  
Zhouquan Ou ◽  
Junming Peng ◽  
Yang Zhang ◽  
Xiaohu Zhang ◽  
...  
Keyword(s):  
Neural Network ◽  
Pid Controller ◽  
Fuzzy Neural Network ◽  
Single Phase ◽  
Fuzzy Inference ◽  
Learning Ability ◽  
Proportional Integral ◽  
Fuzzy Neural ◽  
Grid Connected Inverter ◽  
Self Learning

Based on a single-phase photovoltaic grid-connected inverter, a control strategy combining traditional proportional–integral–derivative (PID) control and a dynamic optimal control algorithm with a fuzzy neural network was proposed to improve the dynamic characteristics of grid-connected inverter systems effectively. A fuzzy inference rule was established after analyzing the proportional, integral, and differential coefficients of the PID controller. A fuzzy neural network was applied to adjust the parameters of the PID controller automatically. Accordingly, the proposed dynamic optimization algorithm was deduced in theory. The simulation and experimental results showed that the method was effective in making the system more robust to external disruption owing to its excellent steady-state adaptivity and self-learning ability.

The Intelligent Scheduling Method of Elevator Group Control Based on Fuzzy Neural Network

2012 ◽  
Vol 155-156 ◽  
pp. 653-657
Author(s):  
Yu Lin Dong ◽  
Xiao Ming Wang
Keyword(s):  
Neural Network ◽  
Control System ◽  
Fuzzy Neural Network ◽  
Control Method ◽  
Scheduling Algorithm ◽  
Mathematic Model ◽  
Group Control ◽  
Fuzzy Neural ◽  
And Performance ◽  
Elevator Group Control

Elevator group control system (EGCS) is a complex optimization system, which has the characteristics of multi-objective, uncertain, stochastic random decision-making and nonlinear. It is hard to describe the elevator group control system in exact mathematic model and to increase the capability of the system with traditional control method. In this paper, we aim at the characters of elevator group control system and intelligent control, introduce the system's control fashion and performance evaluate guidelines and propose an elevator group control scheduling algorithm based on fuzzy neural network.

Development and Research of Intelligent PID Controller Based on Fuzzy Neural Network

2014 ◽  
Vol 644-650 ◽  
pp. 341-345
Author(s):  
Ming Jun Li ◽  
Hua Tian ◽  
Xiao Jing Guo
Keyword(s):  
Neural Network ◽  
Intelligent Control ◽  
Pid Controller ◽  
Fuzzy Neural Network ◽  
Network System ◽  
Validity And Reliability ◽  
Fuzzy Neural ◽  
Neural Network System ◽  
Intelligent Pid ◽  
Matlab Programming

In this paper, intelligent fuzzy control theory is introduced in the model of neural network algorithm, and the neural network system is improved by the PID controller, which has realized the feedback and adjustment function of neural network system, and has made the reaction of the system be more accurate and stable. In order to verify the validity and reliability of the designed intelligent control PID algorithm based on the fuzzy neural network in this paper, the algorithm is carried on the programming by using Matlab programming software, and the control process of PID is calculated by NNbox simulation toolbox, at last, it has obtained the curve of PID control response changing over time. From the response curve, it can be seen that after the PID proportional coefficient is regulated by using fuzzy neural network intelligent control algorithm, it can quickly and steadily obtain the control curve, which has realized better intelligent control effect, and has provided technical reference for the research of intelligent PID controller.

Temperature Control of Batch Reaction Based on Genetic Fuzzy Neural Network

2011 ◽  
Vol 204-210 ◽  
pp. 1968-1971 ◽  
Author(s):  
Chun Tao Man ◽  
Jia Cui ◽  
Xin Xin Yang ◽  
Jun Kai Wang ◽  
Tian Feng Wang
Keyword(s):  
Neural Network ◽  
Temperature Control ◽  
Fuzzy Neural Network ◽  
Control Method ◽  
Design Method ◽  
Batch Reactor ◽  
Batch Processes ◽  
Strong Nonlinearity ◽  
Control Scheme ◽  
Fuzzy Neural

The batch reactor has strong nonlinearity and hysteresis, the conventional control method is hard to meet the control requirements. According to the batch processes temperature control, this thesis proposed an intelligent control scheme. Combined neural networks with fuzzy logic control, searching and optimized parameters of fuzzy neural network by using Genetic Algorithm (GA), displayed the design method and optimization steps, and the simulation results verify the control scheme which proposed is feasible and effective.

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