Passive Model Predictive Impedance Control for Safe Physical Human-Robot Interaction

Various cognitive systems have been designed to model the position and stiffness profiles of human behavior and then to drive robots by mimicking the human's behavior to accomplish physical human-robot interaction tasks through a properly designed impedance controller. However, some studies have shown that variable stiffness parameters of the impedance controller can cause the violation of the passivity constraint of the robot states, and make the robot's stored energy exceed the external energy injected from the human user, thus leading to the unsafe human-robot interaction. To solve this problem, this paper proposes a novel passive model predictive impedance control method including two control loops. In the bottom-loop of the proposed controller, the robot is driven by a variable impedance controller to achieve the desired compliant interaction behavior. In the top-loop of the proposed controller, the model predictive control (MPC) is used to ensure that the robot states satisfy the passivity constraint by calculating a complementary torque to limit the stored energy of the robot. The passivity of the closed-loop robot system and the feasibility of MPC are guaranteed by theoretical analysis, ensuring the safety of the robotic movement in the human-robot interaction. The effectiveness of the proposed method is demonstrated by the simulation and experiment on the Franka Emika Panda robot.