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.

Admittance control is a control scheme to enable physical interactions of a robot, but it easily induces instability when the robot contacts a rigid surface. In this study, a passivity analysis was conducted on a robotic system with admittance control. The results showed that coupled stability with the environment can be ensured when the velocity error between the proxy and the real robot is eliminated. Thus, an adaptive structure modification method is proposed to suppress the possible source of instability. In addition, the energy tank method is combined with the proposed method to ensure system passivity. As a proof of concept, three robot experiments were performed, and the results of the proposed method were compared with those of conventional admittance control and impedance control (with friction compensation). The comparison showed that the proposed method could make the system passive while realizing the desired dynamics during the interaction.

This paper proposes a control structure for accurate tracking and compliant behavior of industrial manipulators without additional sensors. To achieve control objectives, friction, one of the biggest causes of performance degradation, should be compensated. For tracking performance, the estimated friction cancels most friction effects as a feed-forward, and the modified robust control structure eliminates the remaining friction uncertainty, which was originally equivalent to the disturbance observer. For compliant behavior, the compensation force fed to the real plant is bounded in contrast to the conventional disturbance observer structure. The compensation bound could be determined through the experiments. The proposed method is validated by experiments with a 6-DOF collaborative industrial manipulator.

This paper proposes a teleoperation framework to exploit the maximum manipulation capability during teleoperation. Here, exploiting maximum manipulation capacity means that the robot moves with its maximum control input while not violating the given constraints, and it is a nonlinear optimization problem with nonlinear constraints which is hard to be solved. The proposed framework relaxes the optimization problem into a simple QP problem and unifies the various constraints in the joint configuration space and Cartesian task space by utilizing control barrier function and control Lyapunov function techniques. The joint angle, velocity, acceleration limits are imposed on a teleoperated robot so that the robot does not generate any emergency stop during the teleoperation. Especially, the robot shows stable motion even near the kinematic singularity, so the operator can explore almost every reachable and admissible state of the robot via teleoperation.

This paper analyzes the effect of control param-eters of feed-forward and inner loop velocity controller in an admittance control scheme on the performance and passivity. The interaction force, inertia, and damping compensation were considered as the feed-forward input. Sufficient conditions and guidelines for each parameter were provided to enable the implementation of a wide range of desired admittance satis-fying passivity. The proposed guidelines were verified through experiments.