• Proceedings of the KIEE Conference
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• 1998.07c
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• pp.970-972
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• 1998

In this paper, the standard Dole, Glover, Khargoneker, and Francis (abbr. : DGKF 1989) $H_{\infty}$ controller $(H_{\infty}C)$ is extended to the nonlinear feedback linearization-$H_{\infty}$ /sliding mode controller (NFL-$H_{\infty}$/SMC) to solve the problem associated with the full state feedback for the unmeasurable state variables in the conventional SMC, to obtain the smooth control as the linearized controller for a linear system (or to cancel the nonlinearity for the nonlinear system), and to improve the time-domain performance under worst case.

• Proceedings of the KIEE Conference
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• 1996.07b
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• pp.959-961
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• 1996

In this paper, we study the modeling and control of Electro-Magnetic Suspension System with 2 Degree Of Freedom. While the previous researchers considered the control of single rail EMS Systems, we consider the control of two rail EMS Systems. We first derive a simple model to represent the dynamics of EMS System with 2 D.O.F., using the Lagrange's method. The nonlinear equations of motion that we derive are shown to be linearizable by coordinate change and nonlinear static state feedback. The nonlinear static state feedback controller is constructed explicitly.

• Proceedings of the KIEE Conference
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• 2001.11c
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• pp.18-21
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• 2001

In this paper, some geometric condition for a stochastic nonlinear system and an adaptive control method for minimum-phase stochastic nonlinear system using neural network me provided. The state feedback linearization is widely used technique for excluding nonlinear terms in nonlinear system. However, in the stochastic environment, even if the minimum phase linear system derived by the feedback linearization is not sufficient to be controlled robustly. In the viewpoint of that, it is necessary to make an additional condition for observation of nonlinear stochastic system, called perfect filtering condition. In addition, on the above stochastic nonlinear observation condition, I propose an adaptive control law using neural network. Computer simulation shoo's that the stochastic nonlinear system satisfying perfect filtering condition is controllable and the proposed neural adaptive controller is more efficient than the conventional adaptive controller.

• 제어로봇시스템학회:학술대회논문집
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• 1989.10a
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• pp.931-933
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• 1989

If the nonlinear term in a nonlinear control system equation can be deleted by state feedback control, the original system becomes a linear system. For this linear control system, many well known methods may be used to handle it, and then reverse it back to nonlinear form. Many problems of nonlinear control systems can be solved in this way. In this paper, this method will be used to transfer the identification problem of nonlinear systems into a linear control problem. The nonlinear observer is established by constructing linear observer. Then the state control of nonlinear systems is realized. Finally, the technique of the PID controller obtained by using bang-bang tracker as a differentiator provides a stronger robust controller. Even though the method in this paper may not theoretically perfect, many numerical simulations show that it is applicable.

• 제어로봇시스템학회:학술대회논문집
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• 2000.10a
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• pp.18-18
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• 2000

In this paper, some geometric condition for a stochastic nonlinear system and an adaptive control method for minimum-phase stochastic nonlinear system using neural network are provided. The state feedback linearization is widely used technique for excluding nonlinear terms in nonlinear system. However, in the stochastic environment, even if the minimum phase linear system derived by the feedback linearization is not sufficient to be controlled robustly. the viewpoint of that, it is necessary to make an additional condition for observation of nonlinear stochastic system, called perfect filtering condition. In addition, on the above stochastic nonlinear observation condition, I propose an adaptive control law using neural network. Computer simulation shows that the stochastic nonlinear system satisfying perfect filtering condition is controllable and the proposed neural adaptive controller is more efficient than the conventional adaptive controller

• International Journal of Fuzzy Logic and Intelligent Systems
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• v.3 no.1
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• pp.58-65
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• 2003

In this paper, a new model, which is a Takagi-Sugeno fuzzy model, for mobile robot is presented. A controller, consisting of two loops the one of which is the inner state feedback loop designed for stability and the outer loop is a PI controller designed for tracking the reference input, is suggested. Because the robot dynamics is nonlinear, it requires the controller to be insensitive to the nonlinear term. To achieve this objective, the model is developed by well known T-S fuzzy model. The design algorithm of inner state-feedback loop is regional pole-placement. In this paper, regions, for which poles of the inner state feedback loop are lie in, are formulated by LMI's. By solving these LMI's, we can obtain the state feedback gains for T-S fuzzy system. And this paper shows that the PI controller is equivalent to the state feedback and the cost function for reference tracking is equivalent to the LQ(linear quadratic) cost. By using these properties, it is also shown in this paper that the PI controller can be obtained by solving the LQ problem.

• 제어로봇시스템학회:학술대회논문집
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• 2004.08a
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• pp.1906-1908
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• 2004

We propose a result on the stabilization of nonlinear time-delay systems via the feedback linearization method. Using the predictor based control and the parametric coordinate transformation, we introduce a stabilizing controller to compensate time delay. Specifically, we present the delay-dependent stability analysis to makes the considered system stable. Also, an illustrative example is provided

• 제어로봇시스템학회:학술대회논문집
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• 1993.10b
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• pp.480-485
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• 1993

A method for obtaining optimal orbital maneuvers of a space vehicle has been developed by combining feedback linearization method with the elegance of the Lambert's theorem. To obtain solutions to nonlinear orbital maneuver problems. The full nonlinear equations of motion for space vehicle in polar coordinate system are transformed exactly into a controllable linear set in Brunovsky canonical form by using feedback linearization by choosing position vector as fully observable output vector. These equations are used to pose a linear optimal tracking problem with a solutions to Lambert's problem and a linear analytical solution of continuous low thrust problem as reference trajectories.

• Bulletin of the Korean Mathematical Society
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• v.35 no.2
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• pp.325-338
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• 1998

Some nonlocal boundary value problems which arise from a feedback control problem are considered. We give a precise statement of the mathematical problems and then prove the existence and uniqueness of the solutions. We consider the Dirichlet type boundary value problem and the Neumann type boundary value problem with nonlinear boundary conditions. We also provide a regularity results for the solutions.

• 제어로봇시스템학회:학술대회논문집
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• 1996.10b
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• pp.1253-1256
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• 1996

In this paper, a new nonlinear feedback linearization control scheme for induction motors is developed. The control scheme employs a fuzzy nonlinear identification scheme based on fuzzy basis function expansion to adoptively compensate the parameter variations, i.e. rotor resistance, mutual and self inductance etc. An important feature of the proposed control scheme is to incorporate the sliding mode controller into the scheme to speed up convergence rate. Simulation tests show the robust behavior of the proposed controller in the presence of the parameter uncertainties of the machine.