Ensuring Joint Constraints of Torque-Controlled Robot Manipulators under Bounded Jerk

This paper proposes an optimization-based control framework for the torque-controlled robot, which can satisfy the joint position, velocity, and acceleration constraints under a bounded jerk. The optimization filter is incorporated as a module to modify the nominal controller output to ensure joint constraints. To formulate the optimization problem as a QP, the torque optimization problem is converted to the jerk optimization problem using the augmented state, and the constraints are reformulated to be affine in the jerk. Here, the viable constraints are derived using the time-optimal braking policy to guarantee the feasibility of the QP. The proposed method was validated in simulation and with a 6-DOF robot manipulator.