scholarly journals A Robust Adaptive Control using Fuzzy Neural Network for Robot Manipulators with Dead-Zone

2019 ◽  
Vol 14 (5) ◽  
pp. 692
Author(s):  
D. Ha Vu ◽  
Shoudao Huang ◽  
T. Diep Tran ◽  
T. Yen Vu ◽  
V. Cuong Pham
Keyword(s):  
Neural Network ◽  
Fuzzy Neural Network ◽  
Robot Manipulators ◽  
Industrial Robot ◽  
Dead Zone ◽  
Approximation Error ◽  
Robust Adaptive Control ◽  
Neural Network Controller ◽  
Fuzzy Neural ◽  
Robust Adaptive

In this paper, a robust-adaptive-fuzzy-neural-network controller (RAFNNs) bases on dead zone compensator for industrial robot manipulators (RM) is proposed to dead the unknown model and external disturbance. Here, the unknown dynamics of the robot system is deal by using fuzzy neural network to approximate the unknown dynamics. The online training laws and estimation of the dead-zone are determined by Lyapunov stability theory and the approximation theory. In this proposal, the robust sliding-mode-control (SMC) is constructed to optimize parameter vectors, solve the approximation error and higher order terms. Therefore, the stability, robustness, and desired tracking performance of RAFNNs for RM are guaranteed. The simulations and experiments performed on three-link RM are provided in comparison with neural-network (NNs) and proportional-integral-derivative (PID) to demonstrate the robustness and effectiveness of the RAFNNs.

scholarly journals Robust Adaptive Control for Nonlinear Uncertain Systems Using Type-2 Fuzzy Neural Network System

2011 ◽  
Vol 2011 ◽  
pp. 1-25
Author(s):  
Ching-Hung Lee ◽  
Yu-Ching Lin
Keyword(s):  
Neural Network ◽  
Uncertain Systems ◽  
Fuzzy Neural Network ◽  
Learning Algorithm ◽  
Robust Adaptive Control ◽  
Control Scheme ◽  
Fuzzy Neural ◽  
Nonlinear Uncertain Systems ◽  
Robust Adaptive

This paper proposes a novel intelligent control scheme using type-2 fuzzy neural network (type-2 FNN) system. The control scheme is developed using a type-2 FNN controller and an adaptive compensator. The type-2 FNN combines the type-2 fuzzy logic system (FLS), neural network, and its learning algorithm using the optimal learning algorithm. The properties of type-1 FNN system parallel computation scheme and parameter convergence are easily extended to type-2 FNN systems. In addition, a robust adaptive control scheme which combines the adaptive type-2 FNN controller and compensated controller is proposed for nonlinear uncertain systems. Simulation results are presented to illustrate the effectiveness of our approach.

Research on Intelligent Variable Frequency of Air-Conditioner Control System Based on Fuzzy Control

2011 ◽  
Vol 110-116 ◽  
pp. 4076-4084
Author(s):  
Hai Cun Du
Keyword(s):  
Neural Network ◽  
Control System ◽  
Fuzzy Control ◽  
Fuzzy Neural Network ◽  
Air Conditioner ◽  
Neural Network Controller ◽  
The Neural Network ◽  
Network Controller ◽  
Fuzzy Neural ◽  
Fuzzy Control Strategy

In this paper, we determine the fuzzy control strategy of inverter air conditioner, the fuzzy control model structure, the neural network and fuzzy control technology, structural design of the fuzzy neural network controller as well as the neural network predictor FNNC NNP. Simulation results show that the fuzzy neural network controller can control the accuracy greatly improved the compressor, and the control system has strong adaptability to achieve a truly intelligent; model of the controller design and implementation of technology are mainly from the practical point of view, which is practical and feasible.

Application of fuzzy neural network controller in hydropower generator unit

Kybernetes ◽  
2009 ◽  
Vol 38 (10) ◽  
pp. 1709-1717 ◽  
Author(s):  
Zhihuai Xiao ◽  
Jiang Guo ◽  
Hongtao Zeng ◽  
Pan Zhou ◽  
Shuqing Wang
Keyword(s):  
Neural Network ◽  
Fuzzy Neural Network ◽  
Neural Network Controller ◽  
Network Controller ◽  
Fuzzy Neural ◽  
Generator Unit

The Optimal Control of Vehicle`s Steering and Braking System

2013 ◽  
Vol 473 ◽  
pp. 243-246
Author(s):  
Guo Li ◽  
Cheng Yao Jia ◽  
Wen Zheng Zhang
Keyword(s):  
Neural Network ◽  
Fuzzy Neural Network ◽  
Simulation Analysis ◽  
Simulation Software ◽  
Braking System ◽  
Simulink Simulation ◽  
Neural Network Controller ◽  
Network Controller ◽  
Fuzzy Neural ◽  
The Membership Function

In order to make a research on the vehicle`s ABS and AFS system,the fuzzy neural network controller was designed on the basis of the electric vehicle`s steering and braking models. Then the genetic algorithms was used to improve the parameters of the membership function. Finally, the Matlab/Simulink simulation software has been used in the simulation analysis. The result of simulation proves that the designed system has good tracking performance and more stronger systemic robustness .

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