Artificial Neural Network (ANN) based Proportional Integral Derivative (PID) for Arm Rehabilitation Device

salam Jeaeb; Rajaa Gaber; Rasha  Saad Salih; Suaad  Makki Jiaad

doi:10.30572/2018/KJE/160106

Authors

salam waley shneen Energy and Renewable Energies Technology Center, University of Technology – Iraq https://orcid.org/0000-0003-3718-6104
Rajaa khalaf Gaber Electro-mechanical Engineering Department, University of Technology – Iraq
Rasha Saad Salih Electro-mechanical Engineering Department, University of Technology – Iraq
Suaad Makki Jiaad Electro-mechanical Engineering Department, University of Technology – Iraq

DOI:

https://doi.org/10.30572/2018/KJE/160106

Keywords:

Arm Rehabilitation Device, open loop, close loop, ANN, PIDC

Abstract

The current work was developed under the title of Artificial Neural Network (ANN) Proportional Integral Derivative (PID) for the arm rehabilitation device and included building and designing the simulation model and simulation results for the arm rehabilitation device. A set of tests were proposed to include firstly testing a system that represents the state of the open arm rehabilitation device and secondly It represents the closed arm rehabilitation device, third represents the closed-loop arm rehabilitation device with PID control device, fourth represents the arm rehabilitation device using ANN, and finally the closed-loop arm rehabilitation device can be used with a comparison between PIDC and ANN. To conduct all the proposed test cases, a program can be used MATLAB, which can help simulate a device that represents an attempt to regain movement in the arm, which is called rehabilitation. It can be noted that the target group is some people who suffer from stroke. By representing the system in the proposed simulation model, its effectiveness can be verified. It is possible to conduct tests aimed at improving performance by working on developing the model by adopting the appropriate design for the characteristics that match the required operational behavior of the system with all conditions that suit different situations. The test cases demonstrated through the simulation results the possibility of identifying the system behavior for the proposed cases. The difference between the system behavior for all these cases was also identified. In addition to the possibility of improving the performance of the movement recovery device to rehabilitate the injured arm through the system’s performance in the presence of an expert neural network controller, it is better than the traditional controller.

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