Bounded Human–Robot Interaction Control Using Joint Torque Estimation From Electromyographic Signals

Many of the most sophisticated human-robot interaction control schemes fall short when it comes to ensuring safe interactions, primarily because they do not generate bounded control actions. Recent advancements have introduced saturating control schemes to tackle this problem, but these approaches are heavily reliant on precise dynamics of the robotic system. In this context, the paper proposes an innovative adaptive impedance control scheme that utilizes electromyographic (EMG) signals to estimate joint torque, thereby ensuring the generation of bounded control actions to regulate the behavior of exoskeleton-type robotic systems. This scheme is classified as a hybrid controller, which sets it apart from the majority of kinematic controllers that typically use proportional-derivative (PD) or proportional-integral-derivative (PID) control schemes. Unlike these conventional approaches, which depend on accurate mathematical models for effective tracking and stability, the proposed scheme integrates adaptive mechanisms to enhance performance. Validation through numerical simulations and experimental tests using the Hill muscle model confirms the scheme’s effectiveness and reliability in managing both isometric and continuous joint movements, underscoring its significant potential for ensuring safe and adaptive human-exoskeleton interactions.