• Journal of the Korean Society for Precision Engineering
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• v.22 no.4
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• pp.92-98
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• 2005

The learning control develops controllers that learn to improve their performance at executing a given task, based on experience performing this specific task. In a previous work, the authors presented an iterative precision of linear decentralized learning control based on p-integrated learning method for the vertical dynamic multiple systems. This paper develops an indirect decentralized teaming control based on adaptive control method. The original motivation of the teaming control field was loaming in robots doing repetitive tasks such as on an assembly line. This paper starts with decentralized discrete time systems, and progresses to the robot application, modeling the robot as a time varying linear system in the neighborhood of the nominal trajectory, and using the usual robot controllers that are decentralized, treating each link as if it is independent of any coupling with other links. Some techniques will show up in the numerical simulation for vertical dynamic robot. The methods of learning system are shown up for the iterative precision of each link.

• Journal of Institute of Control, Robotics and Systems
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• v.21 no.12
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• pp.1160-1166
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• 2015

This paper proposes a decentralized robust adaptive control scheme for robot manipulators with input torque saturation in the presence of uncertainties. The control system should consider the practical problems that the controller gain coefficients of each joint may be nonlinear time-varying and the input torques applied at each joint are saturated. The proposed robot controller overcomes the various uncertainties and the input saturation problem. The proposed controller is comparatively simple and has no robot model parameters. The proposed controller is adjusted by the adaptation laws and the stability of the control system is guaranteed by the Lyapunov function analysis. Simulation results show the validity and robustness of the proposed control scheme.

• Journal of Institute of Control, Robotics and Systems
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• v.15 no.4
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• pp.413-419
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• 2009

A decentralized adaptive control method is proposed for large-scale systems with unknown time-delayed nonlinear interconnections unmatched in control inputs. It is assumed that the time-delayed interaction terms are bounded by unknown nonlinear bounding functions. The nonlinear bounding functions and uncertain nonlinear functions of large-scale systems are compensated by the function approximation technique using neural networks. The dynamic surface control method is extended to design the proposed memoryless local controller for each subsystem of uncertain nonlinear large-scale time delay systems. Therefore, although the interconnected systems consist of a large number of subsystems, the proposed controller can be designed simply. We prove that all the signals in the total closed-loop system are semiglobally uniformly bounded and the control errors converge to an adjustable neighborhood of the origin. Finally, an example is given to demonstrate the effectiveness and applicability of the proposed scheme.

• Proceedings of the KIEE Conference
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• 2003.11b
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• pp.47-50
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• 2003

For large-scale systems which are composed of interconnections of many lower-dimensional subsystems, decentralized control is preferable since it can alleviate the computational burden, avoid communication between different subsystems, and make the control more feasible and simpler. A power system is such a large-scale system where generators are interconnected through transmission lines. Decentralized control is therefore considered for power systems. In this paper, a robust decentralized excitation control scheme for interactions is proposed to enhance the transient stability of multimachine power systems. First we employ a DFL(Direct Feedback Linearization) compensator to rancel most of the nonlinearities; however, the resulting model still contains nonlinear interconnections. Therefore, we design a robust controller in order to deal with Interconnection terms. In this procedure, an upper bound of interconnection terms is estimated by an estimator. The resulting adaptive scheme guarantees the uniform ultimate boundedness of the closed-loop dynamic systems in the presence of the uncertainties.

• Journal of Korean Institute of Industrial Engineers
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• v.23 no.1
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• pp.1-22
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• 1997

Learning control refers to controllers that learn to improve their performance at executing a given task, based on experience performing this specific task. In a previous work, the authors presented a theory of indirect decentralized learning control based on use of indirect adaptive control concepts employing simultaneous identification and control. This paper extends these results to apply to the indirect repetitive control problem in which a periodic (i.e., repetitive) command is given to a control system. Decentralized indirect repetitive control algorithms are presented that have guaranteed convergence to zero tracking error under very general conditions. The original motivation of the repetitive control and learning control fields was learning in robots doing repetitive tasks such as on an assembly line. This paper starts with decentralized discrete time systems, and progresses to the robot application, modeling the robot as a time varying linear system in the neighborhood of the desired trajectory. Decentralized repetitive control is natural for this application because the feedback control for link rotations is normally implemented in a decentralized manner, treating each link as if it is independent of the other links.

• Proceedings of the KIEE Conference
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• 1993.11a
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• pp.320-322
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• 1993

In this paper, the decentralized adaptive control scheme is applied to the control of drum type boiler system. Because the scheme requires the stability of interconnections, static feedback is used to satisfy the requirement of the scheme. From a priori knowledge of the system, system parameter estimates are bounded to prevent parameter drifting. Computer simulation shows that the decentralized adaptive control is suitable for the control of drum type boiler system and robust in the presence of disturbance.

• 제어로봇시스템학회:학술대회논문집
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• 1989.10a
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• pp.183-187
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• 1989

This paper presents on approach to the position control of a robot manipulator by using a decentralized adaptive control scheme. The large scale system is regarded as the system which consists of many subsystems having interconnection. In each subsystem, a local control system is composed by feedforward and feedback component, one computes the nominal torque from the Newton-Euler equation, the other computes the perturbation equation which reduce the position error of the manipulator along the nominal trajectory. A computer simulation studies was conducted to evaluate and compare the performances of the proposed manipulator control scheme with those of the PD control and centralized control schemes.

• 제어로봇시스템학회:학술대회논문집
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• 1987.10b
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• pp.193-197
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• 1987

Decentralized model reference adaptive controller is used to control interconnected system. Influences caused by interactions between each subsystem are regarded as unmodeled dynamics or disturbances, thus decentralized adaptive controller is designed using MRAC algorithms which guarantees robustness. To expand the stability regions of over all system and to improve control performances, higher level controller is introduced to adjust the control factors such as filter band, size of deadzone or maximum norm of parameter. Local controllers for each subsystem are realized in real time and higher level controller has an ability of detecting the instability phenomena and adjusts the local controller by analysis of power spectrum or square sum of tracking errors.

• 제어로봇시스템학회:학술대회논문집
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• 1987.10b
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• pp.47-51
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• 1987

This paper presents a decentralized adaptive control scheme based on Lyapunov design for robotic manipulators, which make possible the joint independent control without neglecting the coupling between the joint motions. The performance of the presented scheme is compared via computer simulations with the conventional joint independent control scheme.

• Proceedings of the Korea Society for Industrial Systems Conference
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• 2006.05a
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• pp.211-217
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• 2006

The learning control develops controllers that learn to improve their performance at executing a given task, based on experience performing this specific task. In a previous work, the authors presented an iterative precision of linear decentralized learning control based on p-integrated learning method for the vertical dynamic multiple systems. This paper develops an indirect decentralized learning control based on adaptive control method. The original motivation of the teaming control field was teaming in robots doing repetitive tasks such as on an assembly line. This paper starts with decentralized discrete time systems, and progresses to the robot application, modeling the robot as a time varying linear system in the neighborhood of the nominal trajectory, and using the usual robot controllers that are decentralized, treating each link as if it is independent of any coupling with other links. Error wave propagation method will show up in the numerical simulation for five-bar linkage as a vertical dynamic robot. The methods of learning system are shown up for the iterative precision of each link at each time step in repetition domain. Those can be helped to apply to the vertical multiple dynamic systems for precision quality assurance in the industrial robots and medical equipments.