Neural Network Based Lower Limb Prosthetic Control Using Super Twisting Sliding Mode Control
| dc.contributor.advisor | Chala Merga (PhD) | |
| dc.contributor.author | Adisu Tadese | |
| dc.date.accessioned | 2025-04-25T08:25:03Z | |
| dc.date.available | 2025-04-25T08:25:03Z | |
| dc.date.issued | 2025-01 | |
| dc.description.abstract | A prosthetic is an artificial limb employed to substitute lost anatomical structures due to disease, injury, or trauma. In this paper, an approach for controlling the prosthetic leg using a surface Electromyography (sEMG) signal, an artificial neural network (ANN), and super twisting sliding mode control (ST-SMC) is presented. The triggering signal is extracted from surface electromyographic (sEMG) signals recorded from the nine muscles at the lower limb, which are grouped into three movement groups. Intense signal analysis that includes filtering, rectification, and linearization is done to obtain the reference sEMG to the NN from the raw sEMG because the raw sEMG data can’t be used as training data for NN. An artificial neural network (ANN) predicts the joint angle for walking, upstairs and downstairs using the processed sEMG signals of the muscles. The super-twisting sliding mode control (ST-SMC) is used to regulate the motion of the prosthetic joints in accordance with the specified reference trajectories. The kinematic model is formulated using forward and inverse kinematics principles, which determine the position and orientation of the prosthetic leg. The dynamic model is based on a fixed coordinate system of human lower leg modeling, formulated by the Euler-Lagrange principle. Rather than using a simple triple pendulum model, the proposed approach presents a more realistic model of the human lower leg. Matlab software’s signal analyzer, neural network fitting packages, and MATLAB/Simulink are used for signal analysis, neural network training, and model simulation. The dynamic system modeling and ST-SMC controller design are implemented. Parameter change analysis and disturbance analysis were done to show that the controller is robust against internal parameter changes and external environmental changes. From the simulation results, it is observed that training the neural network with processed data and implementing the ST-SMC results in increased regression value and decreased mean squared error (MSE) in trajectory tracking instead of using SMC. | |
| dc.identifier.uri | https://etd.aau.edu.et/handle/123456789/5363 | |
| dc.language.iso | en_US | |
| dc.publisher | Addis Ababa University | |
| dc.subject | Prosthesis | |
| dc.subject | surface Electromyography (sEMG) | |
| dc.subject | Artificial neural network (ANN) | |
| dc.subject | Super twisting Sliding Mode Control (ST-SMC). | |
| dc.title | Neural Network Based Lower Limb Prosthetic Control Using Super Twisting Sliding Mode Control | |
| dc.type | Thesis |