Multi-Priority Cartesian Impedance Control Based on Quadratic Programming Optimization

In this work we introduced a prioritized Cartesian impedance control under the framework of the Quadratic Programming (QP) optimization. In particular, we present a formulation which is simpler than full inverse dynamics, avoids any matrix pseudo-inversion, inverse kinematics computation and considers strict priorities among tasks. Our formulation is based on QP optimization permitting to take into account also explicit inequality constraints. We compare in simulation the tracking results obtained with a classical algebraic implementation against those derived from the proposed QP implementation taking into account joint torque limits. We consider the classical Cartesian impedance controller and a simplified version, also known as Virtual Model Control. Finally the proposed method was implemented and validated on a humanoid upper-body torque controlled robot. Experimental trials involving various physical interaction conditions were executed to demonstrate the performance of the proposed method.

Authors

• Enrico Mingo Hoffman
• Arturo Laurenzi
• Luca Muratore
• Nikos G. Tsagarakis
• Darwin G. Caldwell

Keywords

• Task analysis
• Impedance
• Robots
• Force
• Torque
• Optimization
• Acceleration
• Quadratic Programming
• Impedance Control
• Quadratic Programming Optimization
• Cartesian Impedance Control
• Physical Interaction
• Inequality Constraints
• Torque Control
• Inverse Kinematics
• Inverse Dynamics
• Joint Torque
• Torque Limits
• Dynamical
• Optimal Control
• External Force
• Efficient Control
• Inverse Problem
• Contact Force
• Null Space
• Secondary Task
• Humanoid Robot
• Quadratic Programming Problem
• Virtual Components
• Joint Acceleration
• Real Hardware
• Priority Level
• End-effector Pose
• Cartesian Position
• Robot Structure
• MATLAB Simulation
• Velocity Error