• Proceedings of the KIEE Conference
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• 2005.10b
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• pp.89-91
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• 2005

In this paper, we propose a robust visual feedback controller with integral action for tracking control of n-link robot manipulators in the presence of constant bounded parametric uncertainties. The proposed control input has robustness to the parametric uncertainty and reduces tracking error in the steady-state. The stability of the closed-loop system is shown by Lyapunov method. The effectiveness of the proposed method is shown by simulation results on the 5-link robot manipulators with two degree of freedom.

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

• Journal of Institute of Control, Robotics and Systems
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• v.10 no.1
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• pp.10-14
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• 2004

We present a predictive control algorithm combined with the robust robot control that is constructed on the Lyapunov min-max approach. Since the control design of a real manipulator system may often be made on the basis of the imperfect knowledge about the model, it is an important trend to design a robust control law that guarantees the desired properties of the manipulator under uncertain elements. In the preceding robust control work, we need to tune several control parameters in the admissible set where the desired stability can be achieved. By introducing an optimal predictive control technique in robust control we can find out much more deterministic controller for both the stability and the performance of manipulators. A new class of robust control combined with an optimal predictive control is constructed. We apply it to a simple type of 2-link robot manipulator and show that a desired performance can be achieved through the computer simulation.

• Transactions of the Korean Society of Mechanical Engineers A
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• v.25 no.2
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• pp.192-197
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• 2001

Recent requirements for the fast and accurate motion in industrial robot manipulators need more advanced control techniques. To satisfy the requirements, importance of the force control is being continuously increased and the expensive force sensor is often installed to obtain the contact force information in practice. This information is indispensable for the force control of maintaining the desired contact force. However, the sensor cost is too high to be used in industrial applications. In this paper, it is proposed to estimate the contact force occurred between the end-effector of robots and environment in 3-D. The contact force monitoring system is developed based on the static and dynamic models of 3 DOF robot manipulators, where the contact force is described with respect to the link torque. The Extended Kalman Filter is designed and its performance is verified in simulations.

• The Transactions of the Korean Institute of Electrical Engineers
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• v.41 no.5
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• pp.551-561
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• 1992

A neural optimization network and fuzzy rules are proposed to control the redundant robot manipulators in an environment with obstacle. A neural optimization network is employed to solve the optimization problem for resolved motion control of redundant robot manipulators in an environment with obstacle. The fuzzy rules are proposed to determine the weights of neural optimization networks to avoid the collision between robot manipulators and obstacle. The inputs of fuzzy rules are the resultant distance and change of the distance and sum of the changes by differential motion of each joint. And the output of fuzzy rules is defined as the capability of collision avoidance of joint differential motion. The weightings of neural optimization networks are adjusted according to the capability of collision aboidance of each joint. To show the validities of the proposed method, computer simulation results are illustrated for the redundant robot of the planar type with three degrees of freedom.

• Journal of the Korean Society for Precision Engineering
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• v.14 no.7
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• pp.73-81
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• 1997

An new class of robust position/force hybrid control law is proposed for uncertain robot manipulators. The uncertainty is nonlinear and (plssibly fast) time-varying. Therefore, the uncertain factors such as imper- fect modeling, friction, payload change, and external disturbance are all addressed. Based on the possible bound of the uncertainty, the controller is constructed and the stability study based on Lyapunov function is presented. To show that the proposed control laws are indeed applicable, the theoretical result is applied to a SCARA-type robot manipulator and simulation result is presented.

• Transactions of the Korean Society of Mechanical Engineers A
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• v.21 no.8
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• pp.1209-1217
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• 1997

This paper proposes the modeling of the dynamics of two cooperating robot manipulators performing the assembly job such as peg-in-hole while coordinating the payload along the desired path. The mass and moment of inertia of the manipulators and the payload are assumed to be unknown. To control the uncertain system, a robust control algorithm based on the computed torque control is proposed. Usually, the robust controller requires high input torques such that it may face input saturation in actual application. In this reason, the robust control algorithm includes fuzzy logic such that the magnitude of the input torque of the manipulators is controlled not to go over the hardware saturation while keeping path tracking errors bounded. A numerical example using dual three degree-of-freedom manipulators is shown.

• 제어로봇시스템학회:학술대회논문집
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• 2003.10a
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• pp.354-359
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• 2003

In this paper, we consider the problem of designing a simple learning variable structure system for repeatable tracking control of robot manipulators. We combine a variable structure control law as the robust part for stabilization and a feedforward learning law as the intelligent part for nonlinearity compensation. We show that the tracking error asymptotically converges to zero. Finally, we give computer simulation results in order to show the effectiveness of our method.

• Proceedings of the KIEE Conference
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• 1991.11a
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• pp.409-411
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• 1991

This paper presents a collision avoidance scheme for redundant robot manipulators based on the Configuration Control Scheme. We set a center line through the free space. And we use the redundancy to put the robot links, around the center line as close as possible to avoid the collision with obstacles. Computer simulation shows the effectiveness of this approach.

• 제어로봇시스템학회:학술대회논문집
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• 1996.10b
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• pp.581-584
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• 1996

This paper presents the robust control of robot manipulators using a decentralized control scheme. The control scheme decouples the coupling dynamics between the joints and compensates the joint variable errors without any computation of the dynamics. The performance of the control scheme is compared with that of other control schemes such as the computed torque scheme and the adaptive control scheme by simulation.