Output‐constrained adaptive optimal control for physical human–robot interaction using electromyography signals

Abstract This paper investigates the output‐constrained control problem for physical human–robot interaction using electromyography signals. An interaction force estimation method based on surface electromyography (sEMG) signals is introduced, where random forest is provided to extract interaction force information from sEMG signals. Using sEMG, the interaction force between the robotic and human operator can be separated from that in the environment. An adaptive optimal tracking controller based on robot velocity prediction error feedback is designed, in which an asymmetric time‐varying integral barrier Lyapunov function is used to deal with dynamic spatial constraints. Finally, stability analysis is provided, and the effectiveness is verified by experiments based on a 7‐degree‐of‐freedom Franka Panda robot.