Dereje Shiferaw (PhD)Amanual Tesfaye2023-12-052023-12-052023-02http://etd.aau.edu.et/handle/123456789/234In this thesis, an approach for controlling an artificial hand mechanism using surface Electromyography (sEMG) signal, artificial neural network(ANN) and sliding mode control (SMC) is presented. An artificial hand mechanism also known as a prosthetic hand or bionic hand, is device designed to replace the function of a missing or non-functioning hand. The 3D model of five finger robotic hand was designed using SolidWorks software and then exported to MATLAB/Simulink. This model was then used to extract the kinematic and dynamic properties of the robotic hand for which controller design can be performed and tested. The kinematic model was used to calculate the position and orientation of the hand, while the dynamic model was used to determine the forces and torques required to move the hand fingers to achieve the desired gesture. To use this robotic hand as a replacement for an actual hand lost due to a variety of reasons, integration of this electromechanical system and human biological system is required. To this end, surface Electromyography (sEMG) was used to detect hand muscle activities which were interpreted into six specific hand movements (gestures). By sensing the activities of two muscle groups of the user’s arm, the fingers in the robotic hand was controlled to follow specific trajectories. The classification of the sEMG signals into one of the six gestures was done using Long Short-Term Memory (LSTM) neural network that was trained from a dataset collected from five people. To improve the performance of the LSTM during classification, feature extraction operation of the sEMG signal was performed during training and classification. Feature extraction methods are applied to identify relevant patterns and characteristics from the raw sEMG signals. These characteristics can subsequently be used to categorize and distinguish between various hand movements, enabling the control of the hand using sEMG signals. As a result, the neural network’s accuracy rose from 58.64% when utilizing raw data to 99.3% when using extracted features. Once the required hand gesture was identified from the sEMG signal, interpretation of the gesture into individual fingers joint angles was done using cubicpolynomial path-planning algorithm. These joint angle trajectories were used to command the robotic hand using Sliding-Mode Control (SMC) controller. The reason behind using SMC was its robustness in handling external disturbance, parameter uncertainty and unmodelled dynamics, which are inherent in robotic systems. To test the performance of the controller, parametric uncertainty was added to the system dynamics during simulation. The results show that the SMC controller effectively tracked the desired trajectory in the presence of ±30% link mass variation from their nominal values.en-USThesis