System identification of biped robot based on dynamic fuzzy neural network and improved RBF neural network

2016 ◽  
Author(s):  
Xiaoguang Wu ◽  
Tianci Zhang ◽  
Lei Wei ◽  
Ping Xie ◽  
Yihao Du
Keyword(s):  
Neural Network ◽  
System Identification ◽  
Fuzzy Neural Network ◽  
Rbf Neural Network ◽  
Biped Robot ◽  
Fuzzy Neural

Optimization of Biped Gait Synthesis Using Fuzzy Neural Network Controller

2005 ◽  
Author(s):  
Arbnor Pajaziti ◽  
Ismajl Gojani ◽  
Ahmet Shala ◽  
Peter Kopacek
Keyword(s):  
Neural Network ◽  
Path Planning ◽  
Fuzzy Neural Network ◽  
Biped Robot ◽  
Step Length ◽  
Destructive Testing ◽  
Neural Network Controller ◽  
Network Controller ◽  
Fuzzy Neural ◽  
Motion Range

The Biped Robots have specific dynamical constraints and stability problems, which reduce significantly their motion range. In these conditions, path planning and tracking becomes very important. The joint profiles have been determined based on constraint equations cast in terms of step length and high, step period, maximum step height etc. In this paper Fuzzy Neural Network Controller for Path-Planning and Tracking on incline terrain (up stairs) of a planar five-link Biped Robot is presented. The locomotion control structure is based on integration of kinematics and dynamics model of Biped Robot. The proposed Control Scheme and Fuzzy Neural Algorithm could be useful for building an autonomous non-destructive testing system based on Biped Robot. Structure of Fuzzy Neural Network Controller is optimized using Genetic Algorithm. The effectiveness of the method is demonstrated by simulation example using Matlab software.

scholarly journals Fuzzy Neural Network Q-Learning Method for Model Disturbance Change: A Deployable Antenna Panel Application

2019 ◽  
Vol 2019 ◽  
pp. 1-21
Author(s):  
Zhiyong Liu ◽  
Hong Bao ◽  
Song Xue ◽  
Jingli Du
Keyword(s):  
Neural Network ◽  
Control System ◽  
Basis Function ◽  
Fuzzy Neural Network ◽  
Rbf Neural Network ◽  
Learning Method ◽  
Active Deformation ◽  
Q Learning ◽  
Adjustment Mechanism ◽  
Fuzzy Neural

This paper addresses the disturbance change control problem with an active deformation adjustment mechanism on a 5-meter deployable antenna panel. A fuzzy neural network Q-learning control (FNNQL) strategy is proposed in this paper for the disturbance change to improve the accuracy of the antenna panel. In the proposed method, the error of the model disturbance is reduced by introducing the fuzzy radial basis function (RBF) neural network into Q-learning, and the parameters of the fuzzy RBF neural network were optimized and adjusted by a Q-learning method. This allows the FNNQL controller to have a strong adaptability to deal with the disturbance change. Finally, the proposed method has been adopted in the middle plate of a 5-meter deployable antenna panel, and it was found that the method could successfully adapt the model disturbance change in the antenna panel. Results of the simulation also show that the whole control system meets the required accuracy requirements.

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