Model Predictive Interaction Control for Industrial Robots - Gold, Tobias; Völz, Andreas; Graichen, Knut (21st IFAC World Congress, 2020)
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.<br
Keywords: nonlinear model predictive control; physical robot-environment interaction; force control; motion control; hybrid force/motion control; compliance control; stiffness estimation