• 제어로봇시스템학회:학술대회논문집
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• 2001.10a
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• pp.41.3-41
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• 2001

In this paper, we present a decentralized adaptive neural net(NN) controller for the transient stability and voltage regulation of a multimachine power system. First, an adaptively input-output linearizing controller using NN is designed to eliminate the nonlinearities and interactions between generators. Then, a robust control term which bounds terminal voltage to a neighborhood of the operating point within the desired value is introduced using only local information. In addition, we consider input saturation which exists in the SCR amplifier and prove that the stability of the overall closed-loop system is maintained regardless of the input saturation. The design procedure is tested on a two machine infinite bus power system.

• 제어로봇시스템학회:학술대회논문집
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• 1986.10a
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• pp.45-48
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• 1986

In this paper, time optimal positioning control of the robotic manipulator is discussed. The equations for dynamic model of the robotic manipulator are nonolinear, and each link is highly coupled. A feedback linearizing and decoupling transformation makes the dynamic model linearized and decoupled, and optimal control input for the linear and decoupled system is derived.

• 제어로봇시스템학회:학술대회논문집
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• 1994.10a
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• pp.731-736
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• 1994

Experiment on a lab-scale pH process is carried out to evaluate the control performance of the neural linearizing control scheme(NLCS) using a radial basis function(RBF) network which was previously proposed by Kim and Park. NLCS was developed to overcome the difficulties of the conventional neural controllers which occur when they are applied to chemical processes. Since NLCS is applicable for the processes which are already controlled by a linear controller and of which the past operating data are enough, we first control the pH process with PI controller. Using the operating data with PI controller, the linear reference model is determined by optimization. Then, a IMC controller replaces the PI controller as a feedback controller. NLCS consists of the IMC controller and a RBF network. After the learning of the neural network is fully achieved, the dynamics of the process combined with the neural network becomes linear and close to that of the linear reference model and the control performance of the linear control improves. During the training, NLCS maintains the stability and the control performance of the closed loop system. Experimental results show that the NLCS performs better than PI controller and IMC for both the servo and the regulator problems.

• Journal of Institute of Control, Robotics and Systems
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• v.6 no.8
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• pp.727-731
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• 2000

This paper presents a new practical autopilot design approach to acceleration control for tail-controlled STT(Skid-to-Turn) missiles. The approach is novel in that the proposed parametric affine missile model adopts acceleration as th controlled output and considers the couplings between the forces as well as the moments and control fin deflections. The aerodynamic coefficients in the proposed model are expressed in a closed form with fittable parameters over the whole operating range. The parameters are fitted from aerodynamic coefficient look-up tables by the function approximation technique which is based on the combination of local parametric models through curve fitting using the corresponding influence functions. In this paper in order to employ the results of parametric affine modeling in the autopilot controller design we derived a parametric affine missile model and designed a feedback linearizing controller for the obtained model. Stability analysis for the overall closed loop sys-tem is provided considering the uncertainties arising from approximation errors. the validity of the proposed modeling and control approach is demonstrated through simulations for an STT missile.

• 제어로봇시스템학회:학술대회논문집
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• 1988.10a
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• pp.148-153
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• 1988

In this study, the stabilization of moving sight using a gyro is investigated. At first, Linear Compensator was design by linearizing gyro, torque motor and several parameters from a given required frequency response curve. By using this, System Control Performance was analyzed by back EMF, torque saturation and Coulomb friction effects. Also stabilization Performance by disturbances and Paramter variations were simulated.

• 제어로봇시스템학회:학술대회논문집
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• 1996.10b
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• pp.249-252
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• 1996

The inverted pendulum system is mechanical system which can handle the modern control theory and practical applications. In theoretical field, it is used as the experimental device identifying the effects of control method and in applicative field. There are difficulties in designing or linearizing the practical controller because it is so sensitive to the parameter variation and has the highly non-linear characteristic. In this paper, we suggested the systems which compensate the non-linearity throughout the internal control method and designed controller which is robust to the parameter variation using sliding mode.

• 제어로봇시스템학회:학술대회논문집
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• 1997.10a
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• pp.597-600
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• 1997

In this paper, the authors propose a new method for linearizing a nonlinear dynamical system by use of polynomial compensation. In this method, an M-sequence is applied to the nonlinear system and the crosscorrelation function between the input and the output gives us every crosssections of Volterra kernels of the nonlinear system up to 3rd order. We construct a polynomial compensation function from comparison between lst order Volterra kernel and high order kernels. The polynomial compensation function is, in this case, of third order whose coefficients are variable depending on the amplitude of the input signal. Once we can get compensation function of nonlinear system, we can construct a linearization scheme of the nonlinear system. That is. the effect of second and third order Volterra kernels are subtracted from the output, thus we obtain a sort of linearized output. The authors applied this method to a saturation-type nonlinear system by simulation, and the results show good agreement with the theoretical considerations.

• Proceedings of the KIEE Conference
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• 1996.07b
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• pp.930-932
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• 1996

Some results and a nonlinear controller are proposed for feedback linearizable SISO systems with unknown constant parameters. It is shown that the systems which satisfy the proposed conditions can be transformed into a controllable linear subsystem with unknown parameter and it can be stabilized using the high gain nonlinear feedback linearizing controller. As an example for the proposed theorem, we introduce the single link robot with joint flexibility which is an well known example.

• 제어로봇시스템학회:학술대회논문집
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• 2001.10a
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• pp.95.4-95
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• 2001

In this paper, we proposed and analyze an robust adaptive control scheme for uncertain nonlinear systems using Universal function approximators. The proposed scheme completely overcomes the singularity problem which occurs in the indirect adaptive feedback linearizing control. No projection in the estimated parameters and no switching in the control input are needed. The stability of the closed-loop systems is guaranteed in the Lyapunov standpoint.

• 제어로봇시스템학회:학술대회논문집
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• 2001.10a
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• pp.87.2-87
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• 2001

By the state transformation including independent outputs functions, a nonlinear process model can be decomposed into two subsystems; the one(design model) is described in output variables as new states and used for control system synthesis and the other(disturbance model) is described in the original unavailable states and its couplings with the design model are treated as uncertain time-varying parameters in the design model. Its existence with respect to the design model is ignored. So, the design model is an uncertain time-variant system. Control synthesis based on a reduced design model is a combined ...