In robotic teleoperation, it is crucial to be able to dynamically adjust interactions with the environment. Drawing inspiration from human behavior during interactions, Variable Impedance Control (VIC) has been widely adopted to enhance robotic flexibility and adaptability. However, maintaining the passivity of such control systems remains a critical safety concern. This paper introduces an optimization-based framework for passive variable impedance control in bilateral teleoperation, combining the advantages of Passivity Filters (PFs), Time-Domain Passivity (TDP) control, and Passive-Set-Position-Modulation (PSPM). The method solves an optimization problem aimed at dissipating the energy that could lead to a lack of passivity. The proposed method is assessed through experiments, illustrating its ability to keep the teleoperation system passive and safe under a variable impedance profile.

In this work, we use recent developments in the field of adaptive robust Model Predictive Control (MPC) to build a controller for bilateral teleoperation systems. To guarantee robust constraint satisfaction, we incorporate polytopic tube controllers in the MPC design. In addition, we use online learning methods to learn the environment model. Namely, we use set membership learning to learn the parametric uncertainty bounds and reduce the conservatism of the robust controller, and we combine it with least mean square method to learn a point estimate of the model parameters, which enhances the controller performance. Our simulation demonstrates the effectiveness of the proposed approach in maintaining robust constraint satisfaction and enhancing performance by learning during teleoperation tasks.

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.