Advances in Prosthetic Control: PID to Deep Learning

Abstract

The integration of advanced control strategies in prosthetic limbs has revolutionized the field of biomedical engineering. This paper presents a comprehensive review of control algorithms used in prosthetic devices, focusing on the transition from classical PID control to modern intelligent methods such as fuzzy logic, artificial neural networks (ANNs), and deep learning (DL). The review critically compares the performance, adaptability, and implementation complexity of these techniques in lower and upper limb prosthetics. The paper also highlights current challenges and proposes future research directions for enhancing user comfort, control precision, and real-time adaptability.