User-Tailored Fuzzy-Based Grasp Strength Regulation in Myocontrolled Robotic Hands

Myocontrolled robotic hands require accurate and responsive control to regulate grasp strength effectively. However, many human-in-the-loop (HITL) control systems still lack robust closed-loop solutions for fine grip force regulation, limiting their performance. This paper presents a novel control system for myocontrolled hands that combines contact force sensing and vibrotactile feedback to enable more natural and precise grasp interaction. The system features an advanced force controller based on fuzzy logic, with parameter optimization guided by user preferences collected through a graphical user interface (GUI) using Global Learning of Input-Output Strategies from Pairwise Preferences (GLISp). It is compared against heuristic model and neural network based controllers. The system was validated through real-world experiments using the AR10 robotic hand with OptoForce fingertip sensors, demonstrating improved adaptability and fine force regulation capabilities for the user.