• Journal of the Korean Institute of Intelligent Systems
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• v.22 no.2
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• pp.174-180
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• 2012

This paper proposes a decentralized adaptive output-feedback controller design for nonlinear interconnected systems with unknown time delays. The interaction terms with unknown delays are related to all states of subsystems. The time-delayed functions are compensated by using appropriate Lyapunov-Krasovskii functionals and function approximation technique. The observer dynamic surface design technique is employed to design the proposed memoryless local controller for each subsystem. In addition, we prove that all signals in the closed-loop system are semiglobally uniformly bounded and control errors converge to an adjustable neighborhood of the origin.

• Journal of IKEEE
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• v.3 no.2 s.5
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• pp.250-258
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• 1999

This paper presents a decentralized adaptive controller design for a Magnetically Levitated Fine Manipulator to follow the given trajectory as close as possible in spite of coupling effects between motion axes(degree of freedoms or subsystems). The present controller consists of two parts: the model reference controls based on known subsystems and the local adaptive controls. The former stabilizes the motion of the manipulator so as to follow that of the reference model. The latter reduces tracking errors due to coupling disturbances by adjusting the local gains to such levels that override interactions and assure the stability of the overall system. Through several experimental results, it has been shown that the decentralized adaptive control scheme has better tracking performances comparing to the PID controller case as well as good disturbance(coupling) rejection property.

• Journal of the Korean Institute of Telematics and Electronics B
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• v.29B no.10
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• pp.53-58
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• 1992

This paper presents a decentralized model reference adaptive control scheme for an interconnected linear system composed of a number of single-input single-output subsystems in which outgoing interactions pass through the measurement channel and are subjected to bounded external disturbances. The scheme can treat the unknown strength of interactions as well as uncertainties in subsystem dynamics, and allows for the case when the relative degree of each decoulped subsystem does not exceed two.

• Proceedings of the Korea Association of Information Systems Conference
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• 1996.11a
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• pp.217-227
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• 1996

The new field of learning control develops controllers that learn to improve their performance at executing a given task, based on experience performin this specific task. In a previous work［6］, the authors presented a theory of indirect learning control based on use of indirect adaptive control concepts employing simultaneous identification ad control. This paper develops improved indirect learning control algorithms, and studies the use of such controllers in decentralized systems. The original motivation of the learning control field 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 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. The basic result of the paper is to show that stability of the indirect learning controllers for all subsystems when the coupling between subsystems is turned off, assures convergence to zero tracking error of the decentralized indirect learning control of the coupled system, provided that the sample time in the digital learning controller is sufficiently short.

• The Transactions of the Korean Institute of Electrical Engineers
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• v.34 no.3
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• pp.89-96
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• 1985

This paper presents a decentralized adaptive scheme to stabilize a class of large-scale discrete-time linear systems subject to system parameter uncertainties. The scheme combines an adaptive nonlinear feedback control for compensating some effects by unknown system parameters and the exact model-based linear feedback control for overriding the unfavorable effects by interconnections. A condition of stability is derived, under which the overall adaptive system is assured to be globally stable. Also, a numerical example is provided to illustrate the feasibility of the scheme.

• Proceedings of the Korean Society of Machine Tool Engineers Conference
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• 1996.10a
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• pp.6-10
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• 1996

This paper presents an overview of research results obtained by the authors in a series of publications. Methods are developed both for time-varying and time-invariant for linear and nonlinear. for time domain and frequency domain . and for discrete-time and continuous-time systems. Among the topics presented are: 1. Learning control based on integral control concepts applied in the repetition domain. 2. New algorithms that give improved transient response of the indirect adaptive control ideas. 4. Direct model reference learning control. 5 . Learning control based frequency domain. 6. Use of neural networks in learning control. 7. Decentralized learning controllers. These learning algorithms apply to robot control. The decentralized learning control laws are important in such applications becaused of the usual robot decentralized controller structured.

• Proceedings of the KIEE Conference
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• 2003.11c
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• pp.657-660
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• 2003

Control of multi-link robot arms is a very difficult problem because of the highly nonlinear dynamics. Decentralized control scheme is developed for control of robot manipulators based on RBF(Radial Basis Function) Neural Networks. RBF Neural Networks is used to approximate the coupling forces among the joints, coriolis force, centrifugal force, gravitational force, and frictional force. The compensation controller is also proposed to estimate the bound of approximation error so that the chattering effect of the control effort can be reduced. The proposed scheme does not require an accurate manipulator dynamic, and it is proved that closed-loop system is asymptotic stable despite the gross robot parameter variations. Numerical simulations for two-link robot manipulator are included to show the effectiveness of controller.

• The Transactions of the Korean Institute of Electrical Engineers
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• v.37 no.12
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• pp.879-883
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• 1988

In this paper, self tuning control of interconnected systems are dealt in view point of large scale system control. The plant model is given in MIMO ARMA procss. This process is simlified as independent SISO ARMA processes having offset terma, which are considered as effects of interconnections. In each decentralized system, self tuning controller with instrumental variable method is adopted. As a result, this algorithm enables the paramter estimation to be unbiased and non-drift. This controller contains a new implicit offset rejection technique. Simulation results consider well with the analysis in case of linear interconnection.

• The Transactions of the Korean Institute of Electrical Engineers A
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• v.48 no.9
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• pp.1119-1125
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• 1999

In this paper, a decentralized multilevel-adaptive controller for a boiler-turbine system is designed by using multiple model adaptive method. It is applied to the drum type boiler-turbine system which is simplified from Boryung T/P #1,2 model. A linearlized model is decomposed into three subsystems by means of linear transformation. Then the DMC based on such subsystem is designed and a Multiple Model Adaptive Control(MMAC) scheme is applied for the purpose of the good tracking to variable load demands of the thermal power plant. The good performance of the designed controller is shown by simulations in various conditions that have the large step and ramp change of power demamd.

• Journal of the Institute of Convergence Signal Processing
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• v.19 no.2
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• pp.77-88
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• 2018

This paper proposes a RBFNN(Radial Basis Function Neural Network) based decentralized adaptive tracking control scheme using PSO(Particle Swarm Optimization) for an uncertain electrically driven robot system with input saturation. Practically, the magnitudes of input voltage and current signals are limited due to the saturation of actuators in robot systems. The proposed controller overcomes this input saturation and does not require any robot link and actuator model parameters. The fitness function used in the presented PSO scheme is expressed as a multi-objective function including the magnitudes of voltages and currents as well as the tracking errors. Using a PSO scheme, the control gains and the number of the RBFs are tuned automatically and thus the performance of the control system is improved. The stability of the total control system is guaranteed by the Lyapunov stability analysis. The validity and robustness of the proposed control scheme are verified through simulation results.