• Proceedings of the KIEE Conference
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• 2007.07a
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• pp.1728-1729
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• 2007

This paper presents an adaptive neural dynamic surface control (DSC) approach with $H_{\infty}$ tracking performance for a full dynamics of a nonlinear flight system. It is assumed in this paper that model uncertainties such as structured and unstrutured uncertainties and external disturbances influence the nonlinear aircraft model. In our control system, self recurrent wavelet neural networks (SRWNNs) are used to compensate model uncertainties of the nonlinear flight system, and an adaptive DSC technique is extended for disturbance attenuation of the nonlinear flight system. From Lyapunov stability theorem, it is shown that $H_{\infty}$ performance from external disturbances can be obtained. Finally, we perform the simulation for the nonlinear six-degree-of-freedom F-16 aircraft model to confirm the effectiveness of the proposed control system.

• Ocean Systems Engineering
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• v.5 no.3
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• pp.161-179
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• 2015

An emergency response system (ERS) for vessel oil spills is a complex and dynamic system comprising a number of subsystems and activities. Failures may occur during the emergency response operations, this has negative impacts on the effectiveness of the ERS. Of the classes of problems in analyzing failures, the lack of quantitative data is fundamental. In fact, most of the empirical data collected via questionnaire survey is subjective in nature and is inevitably associated with uncertainties caused by the human being's inability to provide complete judgement. In addition, incomplete information and/or vagueness of the meaning about the failures add difficulties in evaluating the effectiveness of the system. Therefore this paper proposes a framework to evaluate the ERS effectiveness by using the combination of fuzzy reasoning and evidential synthesis approaches. Based on analyzing the procedure of ERS for oil spills, the failures in the system could be identified, using Analytic Hierarchy Process(AHP)to determine the relative weight of identified failures. Fuzzy reasoning combined with evidential synthesis is applied to evaluate the effectiveness of ERS for oil spills under uncertainties last. The proposed method is capable of dealing with uncertainties in data including ignorance and vagueness which traditional methods cannot effectively handle. A case study is used to illustrate the application of the proposed method.

• Proceedings of the KIEE Conference
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• 2006.10c
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• pp.315-317
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• 2006

In this paper, we design a terminal sliding mode controller based on neural network for nonlinear systems with uncertainties. Terminal sliding mode control (TSMC) method can drive the tracking errors to zero within finite time. Also, TSMC has the advantages such as improved performance, robustness, reliability and precision by contrast with classical sliding mode control. For the control of nonlinear system with uncertainties, we employ the self-recurrent wavelet neural network(SRWNN) which is used for the prediction of uncertainties. The weights of SRWNN are trained by adaptive laws based on Lyapunov stability theorem. Finally, we carry out simulations to illustrate the effectiveness of the proposed control.

• Journal of the Korean Society for Precision Engineering
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• v.23 no.4 s.181
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• pp.44-51
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• 2006

This paper addresses sliding mode control of the anti-lock braking system (ABS) with a disturbance observer for model uncertainties such as vehicle parameter variation, un-modeled dynamics, and external disturbances. By using a nominal vehicle model, a sliding mode controller is designed to achieve a desired wheel slip ratio for ABS control. To compensate the model uncertainties, a disturbance observer is introduced with the help of a transfer function of a hydraulic brake dynamics. A proposed sliding mode controller with a disturbance observer is evaluated through simulations for model uncertainties. The simulation results show that the disturbance observer can enhance performances of sliding mode control for ABS.

• 제어로봇시스템학회:학술대회논문집
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• 1990.10a
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• pp.439-445
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• 1990

All models of dynamical systems invariably have some measure of uncertainties associated with some of their dynamics. The recent approaches to establish robustness of stabilizing feedback control against the possible uncertainties have a serious limitation, that is their applicability only to the systems that satisfy the matching conditions. Such conditions are rarely met in general applications. If a particular system satisfies the matching conditions, the addition of an actuator will destroy the satisfaction of such conditions. In this paper, we develop robust control algorithm for uncertain multivariable systems in which the matching conditions are not necessarily met. We empoly Lyapunov's second method to derive robust stabilizing controllers which guarantee asymptotic stability against prescribed uncertainties. The derivation consists of transforming the original uncertain system to controllable canonical form and constructing a constant switching surface by designing the closed-loop characteristics as a function of the uncertainties. Numerical examples are discussed as illustrations.

• Proceedings of the Korean Society of Machine Tool Engineers Conference
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• 2002.04a
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• pp.543-550
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• 2002

This work is concerned with the estimation of output derivatives and their use for the design of robust controller for linear systems with systems uncertainties due to modeling errors and disturbance. It is assumed that a nominal transfer function model and Quantitative bounds for system uncertainties are known. The developed control schemes are shown to achieve regulation of the system output and ensures boundedness of the system states without imposing any structural conditions on system uncertainties and disturbances. Output derivative estimation is first conducted trough restructuring of the plant in a specific parameterization. They are utilized for constructing robust nonlinear high-gain feedback controller of a SMC(Sliding Mode Controller) Type. The performances of the developed controller are evaluated and shown to be effective and useful through simulation study.

• Proceedings of the Korean Society of Precision Engineering Conference
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• 2002.05a
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• pp.253-258
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• 2002

This work is concerned with the estimation of output derivatives and their use for the design of robust controller for linear systems with systems uncertainties due to modeling errors and disturbance. It is assumed that a nominal transfer function model and quantitative bounds for system uncertainties are known The developed control schemes are shown to achieve regulation of the system output and ensures boundedness of the system states without imposing any structural conditions on system uncertainties and disturbances. Output derivative estimation is first conducted trough restructuring of the plant in a specific parameterization. They are utilized for constructing robust nonlinear high-gain feedback controller of a SMC(Sliding Mode Controller)Type. The performances of the developed controller are evaluated and shown to be effective and useful through simulation study.

• Journal of Institute of Control, Robotics and Systems
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• v.6 no.12
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• pp.1133-1145
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• 2000

In this paper a model following flight control system design using the discrete time quasi-sliding mode control method is described. The quasi-sliding mode is represented as the sliding mode band, not as the sliding surface. The quasi-sliding mode control is composed of the equivalent control for the nominal system without uncertainties and disturbances and the additive control compensating the uncertainties and disturbances. The linearized plant on the equilibrium point is used in designing a flight control system and the stability conditions are proposed for the model uncertainties. Pseudo-state feedback control which uses the model variables for the unmeasured states is proposed. The proposed method is applied to the design of the roll attitude and pitch load factor control of a bank-to-turn missile. The performance is verified through the nonlinear six degrees of freedom flight simulation.

• Proceedings of the KIEE Conference
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• 1998.07c
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• pp.875-879
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• 1998

A fault on the transmission line results in the variation of reactance and parametric uncertainties in the power system dynamics. In this case, we need a robust control to cope with these uncertainties. A sliding mode control, a sort of robust control, is known to be robust to parametric or state-dependent uncertainties if the bounding function of uncertain terms is determined a priori. However, in general, we can not readily determine the bounding function for the complex systems. Hence, in this paper we introduce a fuzzy system which can estimate the bounding function in relatively simple way. By the use of the proposed fuzzy system, determination of bounding function is made easier. We applied the proposed scheme to the stabilization of power system under the sudden fault on the transmission lines. The simulation result verifies the effectiveness of the scheme.

• Journal of Power Electronics
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• v.18 no.1
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• pp.103-115
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• 2018

Parametric uncertainties and inverter nonlinearity exist in the permanent magnet synchronous motor (PMSM) drive system of electrical vehicles, which may lead to performance degradation or failure, and eventually threaten reliable operation. Therefore, a model-free deadbeat predictive current controller (MFDPCC) for PMSM drive systems is proposed in this study. The data-driven ultra-local model of a surface-mounted PMSM (SMPMSM) drive system that consists of parametric uncertainties and inverter nonlinearity is first established through the input and output data of a SMPMSM drive system. Subsequently, MFDPCC is designed. The performance comparisons and analyses of the proposed MFDPCC, the conventional proportional-integral controller, and the model-based deadbeat predictive current controller for SMPMSM drive systems are implemented via system simulation and experimental tests. Results show the effectiveness and technical advantages of the proposed MFDPCC.