• 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.

• Journal of the Korean Institute of Intelligent Systems
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• v.27 no.1
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• pp.15-21
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• 2017

In this paper, the decentralized fuzzy observer design technique is presented for discrete-time nonlinear interconnected systems, which are assumed to be with unknown interconnections. To design the decentralized fuzzy observer, the design problem is considered and the performance function is defined to solve the design problem. Based on the performance function, the sufficient condition is derived for the observer design, and its condition is formulated into linear matrix inequalities. Finally, by the simulation result, the validity of the proposed observer design technique is shown.

• 제어로봇시스템학회:학술대회논문집
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• 1989.10a
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• pp.856-861
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• 1989

The control of a class of large scale systems formed by an arbitrary linear interconnections of linear time-invariant subsystems with unknown parameters is investigated. An approach is developed for improving the robustness of such a large scale system. In doing so, the new parameter adaptation algorithm(PAA) is used and a sufficient condition of stability is discussed by using the sector theory.

• 제어로봇시스템학회:학술대회논문집
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• 1989.10a
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• pp.503-507
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• 1989

A new decentralized adaptive controller design is proposed. In large scale interconnected system with unknown parameters, nonlinearities and bounded disturbances, even though the interconnection is weak, the controller parameter drifts due to the interconnection, so the decentralized adaptive controller comes to be unstable. The proposed new decentralized adaptive controller guarantees exponential convergence of tracking and parameter errors to residual sets which depend on the bound for the local disturbances and interconnections as well as on some arbitrary design parameters.

• 제어로봇시스템학회:학술대회논문집
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• 1986.10a
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• pp.554-559
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• 1986

This paper considers the problem of stabilizing a composite system formed by interconnecting a number of single-input single-output linear continuous systems. The problem is general in the sense that in addition to the standard assumption about the uncertainty of the subsystems, the strength of interconnections is assumed unknown. A method to design a local adaptive feedback control is first presented, and then the resultant closed-loop system is assured to be globally stable. Also, a numerical example is illustrated via computer simulation.

• Journal of the Korean Institute of Telematics and Electronics
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• v.24 no.6
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• pp.930-935
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• 1987

This paper presents a decentralized model reference adaptive control scheme for an interconnected continuous linear system composed of a number of single input single output subsystems. The scheme can treat the unknown strengh of interconnections as well as the uncertainty of subsystems. The scheme automatically adjusts the local feedback gains so that the output of each subsystem exponetially tracks that of the reference model.

• 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.

• International Journal of Control, Automation, and Systems
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• v.5 no.5
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• pp.526-538
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• 2007

This paper presents a neural network based decentralized excitation controller design for large-scale power systems. The proposed controller design considers not only the dynamics of generators but also the algebraic constraints of the power flow equations. The control signals are calculated using only local signals. The transient stability and the coordination of the subsystem control activities are guaranteed through rigorous stability analysis. Neural networks in the controller design are used to approximate the unknown/imprecise dynamics of the local power system and the interconnections. All signals in the closed loop system are guaranteed to be uniformly ultimately bounded. To evaluate its performance, the proposed controller design is compared with conventional controllers optimized using particle swarm optimization. Simulations with a three-machine power system under different disturbances demonstrate the effectiveness of the proposed controller design.