• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 1998.10a
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• pp.123-128
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• 1998

This paper proposes design of LFT-based fuzzy controllers for model-following, which are better than the previous input-output linearization controllers, which are not able to follow the model system states and which do not guarantee the stability of all states. The method proposed in this paper provides a LFT-based Takagi-Sugeno(T-S) fuzzy controller with guaranteed stability and model-following via the following steps: First, using LFT(Linear Fractional Transformation) and T-S fuzzy model, controllers, are obtained. Next, error dynamics are obtained for model-following, and errors go to 0(zero). Finally, a T-s fuzzy controller that can stabilizxe the system with the requirement on the control input satisfied is obtained by solving the LMIs with the MATLAB LMI Control Toolbox and a model-following controller is obtained. Simulations are performed for the LFT-based T-S fuzzy controller designed by the proposed method, which show better performance than the results of input-out ut linearization controller.

• Proceedings of the KIEE Conference
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• 2004.11c
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• pp.672-674
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• 2004

Many nonlinear controllers for the power system are based on nonlinear models involving the power angle as an element of the state, and therefore the reference value for the power angle is needed. As this reference value is not generally available, it is difficult to apply such nonlinear control methods in practice. To deal with this problem, we present an input-output feedback linearizing control scheme by selecting the output as a combination of the squared voltage and the relative frequency. It is shown that the internal dynamics are locally stable with controllable damping, and that the frequency remains bounded for all time. Simulations illustrate the effectiveness of the proposed method.

• Journal of IKEEE
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• v.9 no.1 s.16
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• pp.72-78
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• 2005

This paper describes the design of a robust output-feedback controller for a single-input single-output nonlinear dynamical system with a full relative degree. While all the previous research works on the output-feedback control are based on dynamic observers, a new state estimator which uses the past values of the measurable system output is proposed. We name it backward-difference state estimator since the derivatives of the output are estimated simply by backward difference of the present and past values of the output. The disturbance generated due to the error between the estimated and real state variables is compensated using an additional robustifying control law whose gain is tuned adaptively. Overall control system guarantees that the tracking error is asymptotically convergent and that all signals involved are uniformly bounded. Theoretical results are illustrated through a simulation example of inverted pendulum.

• The Transactions of the Korean Institute of Electrical Engineers
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• v.39 no.7
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• pp.729-739
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• 1990

This paper deals with the fault detection problem in uncertain linear/non-linear systems having both undermodelling and noise. A robust fault detection method is presented which accounts for the effects of noise, model mismatch and nonlinearities. The basic idea is to embed the unmodelled dynamics in a stochastic process and to use the nominal model with a predetermined fixed denominator. This allows the input /output relationship to be represented as a linear function of the system parameters and also facilitate the quatification of the effect of noise, model mismatch and linearization errors on parameter estimation by the Bayesian method. Comparisons are made via simulations with traditional fault detection methods which do not account for model mismatch or linearization errors. The new method suggested in this paper is shown to have a marked improvement over traditional methods on a number of simulations, which is a consequence of the fact that the new method explicitly for the effects of undermodelling and linearization errors.

• 제어로봇시스템학회:학술대회논문집
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• 1989.10a
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• pp.93-98
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• 1989

This paper describes the application of the recently developed feedback linearization technique to designing a new Command to Line-of-Sight (CLOS) guidance law. We show that the CLOS guidance problem can be formulated as a tracking problem. Then, using the feedback input-output linearization technique, we find a new 3dimensional CLOS guidance law that can assure zero miss distance for a randomly maneuvering target. It sheds light on the feedforward acceleration compensation terms used in the conventional CLOS guidance laws to improve the performance. To illustrate further the significance of our result, simulation results are given.

• Proceedings of the KIEE Conference
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• 1991.11a
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• pp.366-369
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• 1991

A nonlinear controller based on feedback linearization method is proposed for an electromagnetic suspension system. After exactly linearizing the system with nonlinear feedback, linear control technique is applied. Modeling of stagger typed magnet is introduced and controlled for not only levitation, but guidance. By the feedback linearization, the nonlinear, MIMO system is linearized and decoupled, so we can use linear control law. The simulation of this system control skim is demonstrated. Robustness properties of the proposed controller with respect to the load variations and external disturbance is also analyzed for a multi input multi output system. In this properties, the boundary of variation is proposed.

• Journal of Institute of Control, Robotics and Systems
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• v.15 no.1
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• pp.23-30
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• 2009

This paper addresses how to make a formation of multiple unmanned aerial vehicles (UAVs) using only the relative range information. Since the relative range can easily be measured by an on-board range sensor like the laser range finder, the proposed method does not require any expensive and heavy wireless communication system to share the navigation information of each vehicle. Based on the two-dimensional (2-D) nonlinear equations of motion, we propose a nonlinear formation controller using the typical input-output feedback linearization method. The performance of the proposed formation controller is verified by various numerical simulations.

• 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.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.66 no.1
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• pp.72-81
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• 2017

We propose a switching control scheme for the control of a ball and beam system. It was reported in [4] that a ball and beam system is a nonlinear system which has an ill-defined relative degree. So, the traditional control approaches have been mostly either Jacobian-based control or approximate input-output linearized control in nature. In this paper, motivated by [7], we combine these two traditional control approaches and operate each controller via a pre-designed switching logic so that the improved control result can be obtained without any excessive use of control input. Switching algorithm is developed based on both analysis and actual experimental observation. We verify the effectiveness of our proposed controller via actual experimental results.

• 제어로봇시스템학회:학술대회논문집
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• 2001.10a
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• pp.110-110
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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 and uncertain time-variant system. Control synthesis based on a reduced design model is a combined form of a time-variant input-output linearization with parameter estimation. The parameter estimation is also based on the design model and it gives the parameter estimates such that the estimated outputs follow the actual outputs in a specified way. The disturbances form disturbance model and as well all the other uncertainties affecting the outputs will be reflected into the estimated parameters used in the linearizing control law.