Radial Basis Function Neural Network-based Control Method for a Upper Limb Rehabilitation Robot

2019 ◽  
Author(s):  
Shuxiang Guo ◽  
Wenyang Gao ◽  
Dongdong Bu
Keyword(s):  
Neural Network ◽  
Radial Basis Function ◽  
Upper Limb ◽  
Basis Function ◽  
Control Method ◽  
Rehabilitation Robot ◽  
Upper Limb Rehabilitation ◽  
Radial Basis ◽  
Limb Rehabilitation

Virtual Reality Simulation of 5dof Upper-Limb Rehabilitation Robot Based on Fuzzy Neural Network

2011 ◽  
Vol 48-49 ◽  
pp. 1345-1350
Author(s):  
Xing Li ◽  
Jian Hui Wang ◽  
Xiao Ke Fang
Keyword(s):  
Neural Network ◽  
Virtual Reality ◽  
Upper Limb ◽  
Evaluation System ◽  
Fuzzy Neural Network ◽  
Rehabilitation Robot ◽  
Virtual Reality Simulation ◽  
Upper Limb Rehabilitation ◽  
Fuzzy Neural ◽  
Limb Rehabilitation

In this paper, aiming at the structure of upper-limb rehabilitation robot, establish the model of algorithmic control based on fuzzy neural network and virtual reality simulation model for 5dof upper-limb rehabilitant robot, and take the elbow joint for example to do simulation analysis. The result of simulation shows the fuzzy neural network control is practicable and its control accuracy takes the precedence over the traditional methods. The virtual-reality simulation of 5dof upper-limb rehabilitation robot, which is benefit to understand the complex relationships among the objects, can emulate the features of real rehabilitation robot, laying a solid foundation for rehabilitation evaluation system and telemedicine.

scholarly journals Fault-Tolerant Active Disturbance Rejection Control of Plant Protection of Unmanned Aerial Vehicles Based on a Spatio-Temporal RBF Neural Network

2021 ◽  
Vol 11 (9) ◽  
pp. 4084
Author(s):  
Lianghao Hua ◽  
Jianfeng Zhang ◽  
Dejie Li ◽  
Xiaobo Xi
Keyword(s):  
Neural Network ◽  
Radial Basis Function ◽  
Flight Control ◽  
Basis Function ◽  
Disturbance Rejection ◽  
Control Method ◽  
Fault Tolerant ◽  
Active Disturbance Rejection ◽  
Radial Basis ◽  
Spatio Temporal

This paper presents a fault-tolerant flight control method for a multi-rotor UAV under actuator failure and external wind disturbances. The control method is based on an active disturbance rejection controller (ADRC) and spatio-temporal radial basis function neural networks, which can be used to achieve the stable control of the system when the parameters of the UAV mathematical model change. Firstly, an active disturbance rejection controller with an optimized parameter design is designed for rthe obust control of a multi-rotor vehicle. Secondly, a spatio-temporal radial basis function neural network with a new adaptive kernel is designed. In addition, the output of the novel radial basis function neural network is used to estimate fusion parameters containing actuator faults and model uncertainties and, consequently, to design an active fault-tolerant controller for a multi-rotor vehicle. Finally, fault injection experiments are carried out with the Qball-X4 quadrotor UAV as a specific research object, and the experimental results show the effectiveness of the proposed self-tolerant, fault-tolerant control method.

scholarly journals Gradient Descent Optimization Control of an Activated Sludge Process based on Radial Basis Function Neural Network

2020 ◽  
Vol 10 (4) ◽  
pp. 6080-6086
Author(s):  
A. Lemita ◽  
S. Boulahbel ◽  
S. Kahla
Keyword(s):  
Neural Network ◽  
Wastewater Treatment ◽  
Differential Equations ◽  
Activated Sludge ◽  
Radial Basis Function ◽  
Basis Function ◽  
Control Method ◽  
Classical Method ◽  
Activated Sludge Process ◽  
Radial Basis

Most systems in science and engineering can be described in the form of ordinary differential equations, but only a limited number of these equations can be solved analytically. For that reason, numerical methods have been used to get the approximate solutions of differential equations. Among these methods, the most famous is the Euler method. In this paper, a new proposed control strategy utilizing the Euler and the gradient method based on Radial Basis Function Neural Network (RBFNN) model have been used to control the activated sludge process of wastewater treatment. The aim was to maintain the Dissolved Oxygen (DO) level in the aerated tank and have the substrate concentration Chemical Oxygen Demand (COD5) within the standard limits. The simulation results of DO show the robustness of the proposed control method compared to the classical method. The proposed method can be applied in wastewater treatment systems.

scholarly journals Intention Recognition in Physical Human-Robot Interaction Based on Radial Basis Function Neural Network

2019 ◽  
Vol 2019 ◽  
pp. 1-8 ◽  
Author(s):  
Zhiguang Liu ◽  
Jianhong Hao
Keyword(s):  
Neural Network ◽  
Radial Basis Function ◽  
Real Time ◽  
Basis Function ◽  
Control Method ◽  
Contact Point ◽  
Estimation Method ◽  
Human Robot Interaction ◽  
Data Matching ◽  
Radial Basis

To solve synchronization movement problem in human-robot haptic collaboration, the robot is often required to recognize intention of the cooperator. In this paper, a method based on radial basis function neural network (RBFNN) model is presented to identify the motion intention of collaborator. Here, the human intention is defined as the desired velocity in human limb model, of which the estimation is obtained in real time based on interaction force and the contact point movement characteristics (current position and velocity of the robot) by the trained RBFNN model. To obtain training samples, adaptive impedance control method is used to control the robot during the data acquisition process, and then the data matching is executed due to the phase delay of the impedance function. The advantage of proposed intention estimation method according to the system real-time status is that the model overcomes the shortcoming of difficult estimating the human body impedance parameters. The experimental results show that this proposed method improves the synchronization of human-robot collaboration and reduces the force of the collaborator.

Upper-limb Rehabilitation Robot Based on Motor Imagery EEG

ROBOT ◽  
2011 ◽  
Vol 33 (3) ◽  
pp. 307-313 ◽  
Author(s):  
Baoguo XU ◽  
Si PENG ◽  
Aiguo SONG
Keyword(s):  
Motor Imagery ◽  
Upper Limb ◽  
Rehabilitation Robot ◽  
Upper Limb Rehabilitation ◽  
Limb Rehabilitation
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