Model Predictive Interaction Control for Industrial Robots

Gold T, Völz A, Graichen K (2020)

Publication Language: English

Publication Type: Conference contribution, Conference Contribution

Publication year: 2020

Pages Range: 10026 - 10033

Event location: Berlin DE

DOI: 10.1016/j.ifacol.2020.12.2696

Abstract

This paper discusses the use of model predictive control (MPC) for industrial
robot applications with physical robot-environmental interaction. A model predictive interaction
control (MPIC) scheme is introduced that deals both with the prediction of the robot motion
and the forces between robot and environment. With regard to the robot motion, either the
rigid body dynamics, a simplified model, or a cascaded control structure can be employed. The
external forces or torques are treated as additional state variables whose dynamics are based on
the elastic behavior of the contact surface. Since the force prediction depends on the knowledge
of the environmental stiffness, a method for online estimation is discussed. The approach allows
to realize different tasks as motion control, compliance control, direct force control as well
as hybrid force/motion control by adjusting the weighting factors in the cost function. The
implementation is based on the nonlinear MPC software Grampc and the library Pinocchio
for computation of rigid body dynamics. Besides comparing the different robot dynamics models,
the approach is demonstrated for a hand-guiding and a table wiping task.

Authors with CRIS profile

Tobias Gold Chair of Automatic Control Andreas Völz Chair of Automatic Control Knut Graichen Chair of Automatic Control

Related research project(s)

Compliance for a robotic assistance system April 1, 2019 - Sept. 30, 2022

How to cite

APA:

Gold, T., Völz, A., & Graichen, K. (2020). Model Predictive Interaction Control for Industrial Robots. In Proceedings of the 21st IFAC World Congress (pp. 10026 - 10033). Berlin, DE.

MLA:

Gold, Tobias, Andreas Völz, and Knut Graichen. "Model Predictive Interaction Control for Industrial Robots." Proceedings of the 21st IFAC World Congress, Berlin 2020. 10026 - 10033.

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