• Journal of the Korea Institute of Information and Communication Engineering
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• v.7 no.8
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• pp.1759-1766
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• 2003

This paper proposes a neuro­fuzzy controllers for trajectory tracking control of robot manipulators. The computed torque method is an effective means for trajectory tracking control. However, the tracking performance of this method is severely affected by the uncertainties of robot manipulators. Therefore, the proposed controller is used to compensate the uncertainties of robot manipulators. In the neuro­fuzzy controllers, the number of fuzzy rules used forty­nine. The effectiveness of the proposed controllers is demonstrated by computer simulations using two­link robot manipulator, As a result, it is confirmed that the output of the proposed neuro­fuzzy controllers can efficiently decrease the uncertainties of robot manipulator.

• Journal of Institute of Control, Robotics and Systems
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• v.14 no.7
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• pp.656-662
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• 2008

A cost-function based on manipulability and compatibility is designed to determine assembly motions of two cooperating manipulators. Assembly motions are planned along the direction maximizing performance indices to improve control performance of the two manipulators. This paper proposes bimanual task compatibility by defining cost functions. The proposed cost functions are applied and compared to the bimanual assembly task. The problem is formulated as a constrained optimization considering assembly constraints, position of the workpieces, and kinematics and redundancy of the bimanual robot. The proposed approach is evaluated with simulation of a peg-in-hole assembly with an L-shaped peg and two 3-dof manipulators.

• Proceedings of the IEEK Conference
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• 1999.06a
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• pp.1021-1024
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• 1999

In this paper, we propose a modified feedback neural network structure for adaptive control of robot manipulators. The proposed structure is that all of network output feedback into hidden units and output units. Learning algorithm is standard back-propagation algorithm. The simulation showed the effectiveness of using the new neural network structure in the adaptive control of robot manipulators.

• International Journal of Control, Automation, and Systems
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• v.6 no.1
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• pp.76-85
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• 2008

Motivated by the fact that in many industrial robots the joint velocity is estimated from position measurements, the trajectory tracking of robot manipulators with output feedback is addressed in this paper. The fact that robot actuators have limited power is also taken into account. Let us notice that few solutions for the torque-bounded output feedback tracking control problem have been proposed. In this paper we contribute to this subject by presenting a theoretical reexamination of a known controller, by using the theory of singularly perturbed systems. Motivated by this analysis, a redesign of that controller is introduced. As another contribution, we present an experimental evaluation in a two degrees-of-freedom revolute-joint direct-drive robot, confirming the practical feasibility of the proposed approach.

• 제어로봇시스템학회:학술대회논문집
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• 2001.10a
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• pp.37.5-37
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• 2001

A robust control for robot manipulators actuated by induction motors using neural networks(NNs) is considered. The control is designed to compensate for nonlinear dynamics associated with the mechanical subsystem and the electrical subsystems only with the measurements of link position, link velocity and stator winding currents. Two-layer NNs are used to approximate unknown functions occurring from parameter variation during backstepping design process. Specially, through the use of nonlinear observers for rotor flux, observed backstepping controller is designed to achieve uniform ultimately bounded link position tracking of the given reference signal ...

• 제어로봇시스템학회:학술대회논문집
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• 1987.10a
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• pp.932-938
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• 1987

The dynamic equations of robotic manipulators can be derived from either Newton-Euler equation or Lagrangian equation. Model parameters which appear in the resulting dynamic equation are the nonlinear functions of both the inertial parameters and the geometric parameters of robotic manipulators. The identification of the model parameters is important for advanced robot control. In the previous methods for the identification of the model parameters, the geometric parameters are required to be predetermined, or the robotic manipulators are required to follow some special motions. In this paper, we propose an approach to the identification of the model parameters, in which prior knowledge of the geometric parameters is not necessary. We show that the estimation equation for the model parameters can be formulated in an upper block triangular form. Utilizing the special structures, we obtain a simplified least-square estimation algorithm for the model parameter identification. To illustrate the practical use of our method, a 4DOF SCARA robot is examined.

• Journal of the Korean Society for Precision Engineering
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• v.15 no.12
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• pp.162-168
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• 1998

The traditional robot manipulators are actuated by continuous range of motion actuators such as motors or hydraulic cylinders. However, there are many applications of mechanisms and robotic manipulators where only a finite number of locations need to be reached, and the robot's trajectory is not important as long as it is bounded. Binary manipulator uses actuators which have only two stable states. As a result, binary manipulators have a finite number of states. The number of states of a binary manipulator grows exponentially with the number of actuators. This kind of robot manipulator has some advantage compared to a traditional one. Feedback control is not required, task repeatability can be very high, and finite state actuators are generally inexpensive. And this kind of robot manipulator has a fault tolerant mechanism because of kinematic redundancy. This paper develops algorithms for kinematics and workspace analysis of a binary manipulator.

• 제어로봇시스템학회:학술대회논문집
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• 1993.10b
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• pp.234-239
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• 1993

Most of the control problem is for the redundant manipulators use the pseudo-inverse control, thit is, the redundancy is resolved by the pseudo-inverse of the Jacobian matrix and then the controller is designed based on this resolution. However, this pseudo-inverse control has some problems when the redundant robot repeats the cyclic tasks. This is because the pseudo-inverse resolution is a local solution that generates the different configurations of the robot arm for the same hand position. Therefore it is necessary to find the global solution that maintains the optimal configuration of the robot for the repetitive tasks. In this paper, we want to propose a redundancy resolution method by the optimal theory that uses the calculus of variation. The problem formulations are : first to convert the optimal resolution problem to an optimal control problem and then to resolve the redundancy using the necessary conditions of optimal control.

• Transactions of the Korean Society of Mechanical Engineers A
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• v.26 no.2
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• pp.357-364
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• 2002

A new chattering free sliding made control is proposed for robot manipulators. The control input is derived from the reaching law and the Lyapunov stability criteria, which is only composed of continuous terms. It has a chattering free characteristics and a concise farm. In implementing procedures, no change of equations is needed. Thus, it does not degrade the original merits of the sliding mode control. And it is applied to a 2-link SCARA robot manipulator. It is shown that the proposed control has good trajectory tracking performance compared with the PD control and the conventional sliding mode control which uses the boundary layer concept.

• KIEE International Transaction on Systems and Control
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• v.12D no.1
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• pp.43-50
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• 2002

In the recent years neural networks have fulfilled the promise of providing model-free learning controllers for nonlinear systems; however, it is very difficult to guarantee the stability and robustness of neural network control systems. This paper proposes an adaptive neural network control for robot manipulators based on the radial basis function netwo.k (RBFN). The RBFN is a branch of the neural networks and is mathematically tractable. So we adopt the RBFN to approximate nonlinear robot dynamics. The RBFN generates control input signals based on the Lyapunov stability that is often used in the conventional control schemes. The saturation function is also chosen as an auxiliary controller to guarantee the stability and robustness of the control system under the external disturbances and modeling uncertainties.