A fuzzy neural network-based fractional-order Lyapunov-based robust control strategy for exoskeleton robots: Application in upper-limb rehabilitation

2021 ◽  
Author(s):  
Amir Razzaghian
Keyword(s):  
Neural Network ◽  
Robust Control ◽  
Fractional Order ◽  
Upper Limb ◽  
Control Strategy ◽  
Fuzzy Neural Network ◽  
Upper Limb Rehabilitation ◽  
Fuzzy Neural ◽  
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.

DYNAMIC RECURRENT NEURAL NETWORK BASED CLASSIFICATION SCHEME FOR MYOELECTRIC CONTROL OF UPPER LIMB REHABILITATION ROBOT

2014 ◽  
Vol 14 (06) ◽  
pp. 1440017 ◽  
Author(s):  
YUDING CUI ◽  
CAIHUA XIONG
Keyword(s):  
Neural Network ◽  
Upper Limb ◽  
Recurrent Neural Network ◽  
Classification Scheme ◽  
Majority Vote ◽  
Upper Limb Rehabilitation ◽  
Linear Discriminant ◽  
Structure Selection ◽  
Post Process ◽  
Limb Rehabilitation

This paper proposes and evaluates the application of a modular dynamic recurrent neural network (DRNN) to classify upper limb motion using myoelectric signals. The DRNN algorithmic issues, including the structure selection, the segmentation of the data and various feature sets such as time-domain features and frequency features, were evaluated experimentally in order to actualize the optimization and configuration of this classification scheme. This was achieved by using a majority vote technique to post-process the output decision stream. The DRNN-based approach was then been compared with two commonly used classification methods: multilayer perceptron (MLP) neural network and linear discriminant analysis (LDA). The DRNN-based motion classification system demonstrated exceptional accuracy and a low computational load for the classification of robust limb motion. The DRNN may also display utility for online training and controlling rehabilitation robots.

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