Robots are more and more often designed in order to perform tasks in synergy with human operators. In this context, a current research focus for collaborative robotics lies in the design of high-performance control solutions, which ensure security in spite of unmodeled external forces. The present work provides a method based on Model Predictive Control (MPC) to allow compliant behavior when interacting with an environment, while respecting practical robotic constraints. The study shows in particular how to define the impedance control problem as a MPC problem. The approach is validated with an experimental setup including a collaborative robot. The obtained results emphasize the ability of this control strategy to solve constraints like speed, energy or jerk limits, which have a direct impact on the operator's security during human-robot compliant interactions.

While impedance control is one of the most commonly used strategies for robot interaction control, variable impedance control is a more recent preoccupation. If designing impedance control with varying parameters allows increasing the system flexibility and dexterity, it is still a challenging issue, as it may result in a loss of passivity of the control system. This has an important impact on the stability and therefore on the safety of the interaction. In this paper, we propose methods to design passivity filters that guarantee passivity of the interaction. They aim at either checking whether a desired impedance profile is passive, or modifying it if required.

In this paper, we present a novel identification approach to model the EMG-Force relationship of the human arm, reduced to a single degree of freedom (1-DoF) for simplicity. Specifically, we exploit the Linear Parameter Varying (LPV) framework. The inputs of the model are the electromyographic (EMG) signals acquired on two muscles of the upper arm, biceps brachii and triceps brachii, and two muscles of the forearm, brachioradialis and flexor carpi radialis. The output of the model is the force produced at the hand actuating the elbow. Because of the position-dependency of the system, the elbow angle is used as scheduling signal for the LPV model. Accurate modeling of the human arm with this approach opens new possibilities in terms of robot control for physical Human-Robot Interaction and rehabilitation robotics.