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This technical note presents a discussion on the dynamic modeling and analysis of the adaptive controller of a Mechanical Prosthetic finger with constraints mass (m) of the mechanical finger and constant friction (K1) with variation in spring constraint (K2) parameter during grasping action. For the design of the adaptive controller and assessment of adaptation gain, an underdamped second-order control system has been considered and variation of adaptation gain (γ) within certain pre-defined limits of system parameters, variation in the adaptation mechanism has been analyzed using Gradient method MIT rule. To ensure global stability along with the convergence on nonconformity of plant parameters Lyapunov Rule has been utilized towards closed-loop asymptotic tracking.

This study describes a new method for gripping and sensing a physical object (or material) with a prosthetic arm that uses haptic technologies, kinaesthetic communication, and machine learning. Haptic technology is a method of determining if an object is firm or soft as if it were gripped by a human and determining how much gripping force the object can withstand without crushing it. The bending moment and gripping force are measured using a flex sensor in human fingers and a pressure sensor applied by the tip of the human fingers. Three different types of objects (soft sponge, hard sponge, and plastic) are studied and tested in this work by pressing them with varying gripping pressures (soft, firm, and firmer). In addition, a model (Haptic Intelligence Recorder arm) is proposed that can anticipate the object type and gripping force based on the recorded intelligence data. The major goal is to educate our prosthetic hand to be able to grip various items with varying finger pressures, much like we can do naturally. Finally, a glove is created that is tailored to the intelligence arm’s ability to anticipate grabbing items.