Adaptive Dual Control for Robust and Scalable Functional Training of Prosthesis

Conventional machine learning paradigms typically operate on fixed datasets. In contrast, neurobiological learning is characterized by plasticity within the central nervous system, whereby neural activity evolves dynamically over time. This introduces a circular challenge: prosthetic control requires stable neural input, yet the neural signals themselves are continuously modified through the process of learning and adaptation. To address this problem, we propose a framework informed by Dual Control Theory and Model Identification Adaptive Control. In this scheme, control of the prosthetic device is initially distributed between automated computational commands and user-generated neural activity. As the neural signals progressively adapt and become more effectively tuned to the prosthetic's movements, control is gradually transferred to the neural domain, culminating in full user-directed operation. Training proceeds incrementally, beginning with a single degree of freedom. Once stable control is achieved, additional degrees of freedom are introduced sequentially, allowing the user to build control capacity in a structured and progressive manner.