• 제어로봇시스템학회:학술대회논문집
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• 1993.10b
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• pp.524-529
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• 1993

Component Cost Analysis considers any given system driven by a white noise process as an interconnection of different components, and assigns a metric called "component cost" to each component. These component costs measure the contribution of each component to a predefined quadratic cost function. One possible use of component costs is for model reduction by deleting those components that have the smallest component cost. The theory of Component Cost Analysis is extended to include finite-bandwidth colored noises. The results also apply when actuators have dynamics of their own. When the dynamics of this input are added to the plant, which is to be reduced by CCA, the algorithm for model reduction process will be called Weighted Component Cost Analysis (WCCA). Closed-form analytical expressions of component costs for continuous time case, are also derived for a mechanical system described by its modal data. This is very useful to compute the modal costs of very high order systems beyond Lyapunov solvable dimension. A numerical example for NASA's MINIMAST system is presented.presented.

• Proceedings of the KIEE Conference
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• 2003.11b
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• pp.187-190
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• 2003

In this paper, we address the robust adaptive backstepping controller using fuzzy neural network (FHIN) for a class of uncertain output feedback nonlinear systems with disturbance. A new algorithm is proposed for estimation of unknown bounds and adaptive control of the uncertain nonlinear systems. The state estimation is solved using K-fillers. All unknown nonlinear functions are approximated by FNN. The FNN weight adaptation rule is derived from Lyapunov stability analysis and guarantees that the adapted weight error and tracking error are bounded. The compensated controller is designed to compensate the FNN approximation error and external disturbance. Finally, simulation results show that the proposed controller can achieve favorable tracking performance and robustness with regard to unknown function and external disturbance.

• Proceedings of the KIEE Conference
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• 2008.10b
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• pp.185-186
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• 2008

In this paper, the problem of stabilization for nonlinear systems with state constraints is addressed. The designed Lyapunov function guarantees that system states remain within constraints for all time and the control law constructed using backstepping renders the origin exponentially stable in the safe region.

• Proceedings of the KIEE Conference
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• 2005.07d
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• pp.2723-2725
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• 2005

In this paper, a robust $H{\infty}$ stabilization problem to a uncertain discrete-time nonlinear systems with time-delay via fuzzy static output feedback is investigated. The Takagi-Sugeno (T-S) fuzzy model is employed to represent an uncertain nonlinear systems with time-delayed state. Then parallel distributed compensation technique is used for designing of the robust fuzzy controller. Using a single Lyapunov function, the globally asymptotic stability and disturbance attenuation of the closed-loop fuzzy control system are discussed. Sufficient conditions for the existence of robust $H{\infty}$ controllers are given in terms of linear matrix inequalities via similarity transform and congruence transform technique.

• 제어로봇시스템학회:학술대회논문집
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• 1988.10a
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• pp.597-602
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• 1988

In this paper, we consider conventional and modified proportional navigation guidance(PNG) laws for a random maneuvering target. By means of Lyapunov function approach, we show that an ideal missile guided by the conventional PNG law can always intercept a random maneuvering target if some specified initial conditions are satisfied and the navigation constant is chosen sufficiently high. In addition, we propose a modified PNG law. At the final phase of pursuit, the proposed guidance law has a better acceleration profile than the conventional PNG law.

• International Journal of Ocean Engineering and Technology Speciallssue:Selected Papers
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• v.5 no.1
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• pp.46-52
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• 2002

A robust nonlinear observer, utilizing the sliding mode concept, is developed for the dynamic positioning of ships. The observer provides the estimates of linear velocities of the ship and bias from slowly varying environmental loads. It also filters out wave frequency motion to avoid wear of actuators and excessive fuel consumption. The main advantage of the proposed observer is in its robustness. Especially, the observer structure with a saturation function makes the proposed observer robust against neglected nonlinearties, disturbances and uncertainties. Since the mathematical model of DP ships is difficult to obtain and includes uncertainties and disturbances, it is very important for the observer to be robust. A nonlinear output feedback controller is derives based on the developed observer using the observer backstepping technique, and the global stability of the observer and control law is shown by Lyapunov stability theory.. A set of simulation was carried out to investigate the performance of the proposed observer for dynamic positioning of ships.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.59 no.7
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• pp.1309-1313
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• 2010

In this paper, we propose a novel guidance law for controlling an UAV(Unmanned Air-Vehicle) to follow a reference line in vertical plane. A kinematics model representing the relative motion of the UAV to the reference line is derived. And then LQR(Linear Quadratic Regulator) theory is applied to the model to derive the VLFG(Vertical Line Following Guidance) law. The resultant guidance law forms a gain-scheduling controller scheduled by a simple parameter $\sigma$ which is a function of the UAV's velocity, axial acceleration, gravity, and the slope of the reference line. Also derived is a stability condition for the $\sigma$ variation based on Lyapunov theory. Simulation results show that the proposed guidance law can be applied effectively to UAV guidance algorithm design.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.13 no.1
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• pp.1-11
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• 1988

In this paper, the boundedness of adjustable parameters for the model reference adaptive control(MRAC) system using a constant reference signal is discussed. This analysis is motivated by that it is possibel to verify the existence, boundedness and bounded range of the parameter as well as the stability of the adaptive system with an alternative propoerty of Lyapunov function. For two adaptive laws; a general gradient mothod(GGM) and a least square method(LSM), unique solution set in parameter space can be estabilished by a new approach suggeste here. Computer simulation results to show the effect of parameter space analysis are also examined.

• Proceedings of the KIEE Conference
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• 1996.11a
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• pp.58-61
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• 1996

For a class of single-input single-output continuous-time nonlinear systems, a multilayer neural network-based controller that feedback-linearizes the system is presented. Control action is used to achieve tracking performance for a state-feedback linearizable but unknown nonlinear system. The multilayer neural network(NN) is used to approximate nonlinear continuous function to any desired degree of accuracy. The weight-update rule of multilayer neural network is derived to satisfy Lyapunov stability. It is shown that all the signals in the closed-loop system are uniformly bounded. Initialization of the network weights is straightforward.

• Proceedings of the KIEE Conference
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• 1996.07b
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• pp.939-942
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• 1996

This paper presents a stable learning algorithm for diagonal recurrent neural network(DRNN). DRNN is applied to a problem of controlling nonlinear dynamical systems. A architecture of DRNN is a modified model of the Recurrent Neural Network(RNN) with one hidden layer, and the hidden layer is comprised of self-recurrent neurons. DRNN has considerably fewer weights than RNN. Since there is no interlinks amongs in the hidden layer. DRNN is dynamic mapping and is better suited for dynamical systems than static forward neural network. To guarantee convergence and for faster learning, an adaptive learning rate is developed by using Lyapunov function. The ability and effectiveness of identifying and controlling a nonlinear dynamic system using the proposed algorithm is demonstrated by computer simulation.