Integrating Neuro-Sliding Mode Control and Spiking Neural Networks for Enhanced Robotic Prosthetics

This article introduces an innovative approach to enhancing the precision and adaptability of prosthetic arms by fusing Neuro-Sliding Mode Control (NSMC) and Spiking Neural Networks (SNN). To address dynamic limb movement complexities and the need for real-time cognitive adaptability, our research integrates NSMC and SNN within a 3 Degree of Freedom (3-DOF) robotic arm framework. NSMC governs dynamic arm motion, ensuring stability and precision, while SNN introduces cognitive elements for learning and user-specific adaptation. This integrated framework promises a harmonious balance between stability and cognitive flexibility, resulting in smoother and more intuitive prosthetic movements. Building on a comprehensive review of prior studies, we identify challenges in joint design, control algorithms, and adaptability, laying the foundation for our research. The article details design considerations, implementation strategies, and experimental validations, highlighting the efficacy of the NSMCSNN integration. Experimental results demonstrate the significantly improved performance of the 3-DOF robotic arm, closely resembling natural human motion. This transformative approach offers a trajectory toward more intuitive and user-friendly robotic prosthetics, with broader implications for the advancement of human-machine interaction in the field of robotic prosthetics. The article ends by exploring potential directions for future research.