Wheeled Robot Based on Fuzzy Neural Network Tracking Studies

2013 ◽  
Vol 373-375 ◽  
pp. 181-184
Author(s):  
Su Ying Zhang ◽  
Shao Jie Xu ◽  
Jing Fei Zhu ◽  
Bing Hao Li ◽  
Wen Pan Shi
Keyword(s):  
Neural Network ◽  
Fuzzy Neural Network ◽  
Control Method ◽  
External Disturbance ◽  
Network Control ◽  
Learning Ability ◽  
Parameter Uncertainties ◽  
Wheeled Robot ◽  
Fuzzy Neural ◽  
Arbitrary Precision

The wheeled robot with non-integrity constraints is a typical nonlinear system, in order to achieve the ideal path tracing, presented a theory based on fuzzy neural network control. Centralized compensation system based on neural network uncertainty can be arbitrary-precision approximation of continuous nonlinear functions as well as the complex uncertainties with adaptive and learning ability. By MATLAB simulation showed that the control method to ensure fast convergence and error robustness of parameter uncertainties and external disturbance.

Fuzzy Neural Network Hybrid Learning Control on AUV

2012 ◽  
Vol 468-471 ◽  
pp. 1732-1735
Author(s):  
Jing Zhao ◽  
Zhao Lin Han ◽  
Yuan Yuan Fang
Keyword(s):  
Neural Network ◽  
Fuzzy Neural Network ◽  
Autonomous Underwater Vehicle ◽  
Learning Algorithm ◽  
Hybrid Learning ◽  
Network Control ◽  
Learning Ability ◽  
B Spline ◽  
Hybrid Learning Algorithm ◽  
Fuzzy Neural

A novel controller based on the fuzzy B-spline neural network is presented, which combines the advantages of qualitative defining capability of fuzzy logic, quantitative learning ability of neural networks and excellent local controlling ability of B-spline basis functions, which are being used as fuzzy functions. A hybrid learning algorithm of the controller is proposed as well. The results show that it is feasible to design the fuzzy neural network control of autonomous underwater vehicle by the hybrid learning algorithm.

scholarly journals Autonomous underwater vehicle precise motion control for target following with model uncertainty

2017 ◽  
Vol 14 (4) ◽  
pp. 172988141771980 ◽  
Author(s):  
Huang Hai ◽  
Zhang Guocheng ◽  
Qing Hongde ◽  
Zhou Zexing
Keyword(s):  
Neural Network ◽  
Fuzzy Neural Network ◽  
Autonomous Underwater Vehicle ◽  
Autonomous Underwater Vehicles ◽  
External Disturbance ◽  
Underwater Vehicle ◽  
Parameter Uncertainties ◽  
Fuzzy Neural ◽  
Recurrent Type

Target following plays an important role in oceanic detection and target capturing for autonomous underwater vehicles. Due to the model nonlinearity and external disturbance, the dynamic model of a portable autonomous underwater vehicle was usually established with parameter uncertainties. In this article, a petri-based recurrent type 2 fuzzy neural network has been built to approximate the unknown autonomous underwater vehicle dynamics. The type 2 fuzzy logic system has been applied to the network to improve the approximation accuracy for systematic nonlinearity, and the petri layer in the network can improve estimation speed and reduce energy consumption. A petri-based recurrent type 2 fuzzy neural network–based adaptive robust controller has been proposed for target tracking. In the offshore experiments, the proposed controller has not only realized stable position and pose control but also successfully followed mobile target on the surface. In the tank underwater experiments, the pipeline target has been successfully followed to further verify the controller performance.

scholarly journals Intelligent control of horizontal vibration of high-speed elevator based on gas–liquid active guide shoes

2021 ◽  
Vol 22 ◽  
pp. 2
Author(s):  
Qin He ◽  
Peng Zhang ◽  
Shunxin Cao ◽  
Ruijun Zhang ◽  
Qing Zhang
Keyword(s):  
Neural Network ◽  
High Speed ◽  
Fuzzy Neural Network ◽  
Control Method ◽  
Mean Value ◽  
Network Control ◽  
Neural Network Control ◽  
Horizontal Vibration ◽  
Suppression Effect ◽  
Fuzzy Neural

Aiming at the inconsistency between the vibration of the car and the car frame in the actual operation of a high-speed elevator and the horizontal vibration caused by the roughness excitation of the guide rail, this study designs a gas–liquid active guide shoe and establishes a horizontal vibration model of the 8-DOF high-speed elevator car system separated from the car and the car frame. Then, the correctness of the model is verified by experiments. Based on this, a fuzzy neural network intelligent vibration reduction controller based on the Mamdani model is designed and simulated by MATLAB. The results show that the root mean square value, mean value, and maximum value of vibration acceleration are reduced by more than 55% after using the fuzzy neural network control method, and the suppression effect is better than that of BP neural network control. Therefore, the intelligent vibration absorption controller designed by this research institute can effectively suppress the horizontal vibration of high-speed elevators.

Fuzzy-Neural Network Control on Secondary Hydraulic Transmission System

2014 ◽  
Vol 945-949 ◽  
pp. 1615-1618 ◽  
Author(s):  
Fa Ye Zang ◽  
Yong Wang ◽  
Xiang Zhen Kong
Keyword(s):  
Neural Network ◽  
Fuzzy Neural Network ◽  
Control Method ◽  
Transmission System ◽  
Network Control ◽  
Neural Network Control ◽  
Transmission Control ◽  
Fuzzy Neural ◽  
Simulation Results ◽  
Adaptive Ability

A secondary hydraulic transmission control method is presented based on the fuzzy-neural network control which introduces the fuzzy control into neural network. The simulation results show the self-adaptive ability and controlling performance of the secondary hydraulic transmission system is improved.

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.

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