• Proceedings of the KIEE Conference
• /
• 1994.11a
• /
• pp.308-310
• /
• 1994

Position control for robot manipulator may not suffice when any contacts are made between the end-effector and various environments. Therefore interaction forces must be controlled in tasks performed by robot manipulator. In general, there are two types of force control for robot manipulator. One is a stiffness control and the other is a hybrid position/force control. Stiffness control is that environment can be modeled as a spring and utilizes the desired normal force to determine the desired normal position. Hybrid position/force control, however, can be used for robot manipulator to track position and force trajectories simultaneously. This paper will compare the result of the hybrid position/force control method with that of the stiffness control method.

• 제어로봇시스템학회:학술대회논문집
• /
• 1992.10a
• /
• pp.638-641
• /
• 1992

In this paper, a hybrid position/force control scheme is proposed. The control scheme modifies the position command for force control against constraint surface of environment and is very simply designed and implemented. The merits of the control scheme are that it can cope with change of constraint conditions and small position inaccuracy of the environment. A constraint surface position observer is also proposed to reduce disturbances on controlled force.

• Journal of Institute of Control, Robotics and Systems
• /
• v.3 no.3
• /
• pp.252-258
• /
• 1997

This paper presents neural networks for hybrid position/force control which is a type of position and force control for robot manipulators. The performance of conventional hybrid position/force control is excellent in the case of the exactly-known dynamic model of the robot, but degrades seriously as the uncertainty of the model increases. Hence, the neural network control scheme is presented here to overcome such shortcoming. The introduced neural term is designed to learn the uncertainty of the robot, and to control the robot through uncertainty compensation. Further more, the learning rule of the neural network is derived and is shown to be effective in the sense that it requires neither desired output of the network nor error back propagation through the plant. The proposed scheme is verified through the simulation of hybrid position/force control of a 6-dof robot manipulator.

• 제어로봇시스템학회:학술대회논문집
• /
• 1996.10b
• /
• pp.743-746
• /
• 1996

A hybrid control scheme to regulate the force and position by dual arms is proposed, where two arms are treated as one arm in a kinematic viewpoint. Our approach is different from other hybrid control approaches which consider robot dynamics, in the sense that we employ a purely kinematic based approach for hybrid control, with regard to the nature of position-controlled industrial robots. The proposed scheme is applied to sawing task. In the sawing task, the trajectory of the saw grasped by dual arms is planned in an offline fashion. When the trajectory of the saw is planned to follow a line in a horizontal plane, 3 position parameters are to be controlled(i.e, two translational positions and one rotational position). And a certain level of contact force has to be controlled along the vertical direction(i.e., minus z-direction) not to loose the contact with the object to be sawn. Typical feature of sawing task is that the contact position where the force control is to be performed is continuously changing. Therefore, the kinematic mapping between the force controlled position and the joint actuators has to be updated continuously. The effectiveness of the proposed control scheme is experimentally demonstrated. The proposed hybrid control scheme can be applied to arbitrary dual arm systems, regardless of their kinematic structure and the number of actuated joints.

• Proceedings of the Korean Society of Precision Engineering Conference
• /
• 1997.04a
• /
• pp.772-776
• /
• 1997

For a robot to perfom more versatile tasks, it is invitable for the robot's end-effector to come into contact with its environment. In thos case, to achieve better performance, it is necessary to properly control the contact force between the robot and the environment. In thos work, hybrid control theory is studied and is verified through experiment using a 3 DOF robot. In the experiment, two position/force controllers are used. Fist, proportional-integral-derivative controller is used as the controller for both position and force. Second, computed-torque method is used as the position controller, and proportional-integral-derivative controller is used as the force controller. For a proper modeling used in computed-torque method, the friction torque is measured by experiment, and compensation method is studied. The hybrid control method used in this experiment effectively control the contact force between the end-effector and the environment for various types of jobs.

• 제어로봇시스템학회:학술대회논문집
• /
• 1997.10a
• /
• pp.253-256
• /
• 1997

This paper proposes a control strategy of position and force control in the same direction based on hybrid position/force control. In order to control position and force in the same direction, a weighting matrix is introduced instead of a selection matrix suggested by Raibert and Craig. The major part of the controller output comes from the position controller when a position control error is large, from the force controller when a position control error is large. The proposed algorithm is implemented by the simulation and experiment focusing on the peg-in-hole task where friction exist significantly and is not constant. It also adopts and event control scheme for more efficient performance.

• Transactions of the Korean Society of Mechanical Engineers A
• /
• v.24 no.3 s.174
• /
• pp.653-660
• /
• 2000

The automatization by industrial robot of today is merely rely on to the simple position repeating works, but requirements of research and development to the force control which would adapt positively to various restriction or contacting works to environment. In this paper, a learning control algorithm using, neural networks is proposed for the position and force control by a direct-drive robot. The proposed controller is the feedback controller to which the learning function of neural network is added on to and has a character of improving controller's efficiency by learning. The effectiveness of the proposed algorithm is demonstrated by the experiment on the hybrid position and force control of a parallelogram link robot with a force sensor.

• 제어로봇시스템학회:학술대회논문집
• /
• 1992.10b
• /
• pp.1-6
• /
• 1992

A principle of "orthogonalization" is proposed as an extended notion of hybrid (force and position) control for robot manipulators under geometric endpoint constraints. The principle realizes the hybrid control in a strict sense by letting position and velocity feedback signals be orthogonal in joint space to the contact force vector whose components are exerted at corresponding joints. This orthogonalization is executed via a projection matrix computed in real-time from a gradient of the equation of the surface in joint coordinates and hence both projected position and velocity feedback signals become perpendicular to the force vector that is normal to the surface at the contact point in joint space. To show the important role of the principle in control of robot manipulators, three basic problems are analyzed, the first is a hybrid trajectory tracking problem by means of a "modified hybrid computed torque method", the second is a model-based adaptive control problem for robot manipulators under geometric endpoint constraints, and the third is an iterative learning control problem. It is shown that the passivity of residual error dynamics of robots follows from the orthogonalization principle and it plays a crucial role in convergence properties of both positional and force error signals.force error signals.

• 제어로봇시스템학회:학술대회논문집
• /
• 1995.10a
• /
• pp.408-411
• /
• 1995

In this paper, we discuss the force control of flexible manipulators. Since the force control of flexible manipulators with planar one or two links using the distributed-parameter modeling has been the subject of a considerable number of publications until now, real time computations of the force control schemes are possible. But, application of those control schemes to multi-link spatial manipulators is fairly complicated. In this paper, we apply a concise hybrid position/force control scheme for a flexible manipulators. We use a lumped-parameter modeling for the flexible manipulators. The Hamilton's principle is applied to derive the equations of motion for the system and then, state-space model is obtained by the Lagrange's method. Finally, comparison of simulation results with experimental results is given to show the performance of our method.

• 제어로봇시스템학회:학술대회논문집
• /
• 1994.10a
• /
• pp.340-345
• /
• 1994

Dynamic hybrid position/force control of flexible manipulators is proposed. First, a 2 D.O.F. flexible manipulator is modeled using the spring-mass model. Second, the equation of motion considering the tip constraints is derived. Third, hybrid position/force control algorithm is derived. In this control algorithm, the differentiable order of the desired trajectory and the stability condition are different from the case of rigid manipulators. Lastly, to verify the effectiveness of the proposed control algorithm, simulation results are presented.