• The Transactions of The Korean Institute of Electrical Engineers
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• v.67 no.2
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• pp.261-269
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• 2018

In this paper, we propose a Takagi-Sugeno (T-S) fuzzy-model-based design for the tracking control of a class of nonlinear underwater glider. By using the partial linearization and the sector nonlinearity, the underwater glider with six degrees of freedom (6 DOF) is modelled by the T-S fuzzy model. The concerned tracking control problem with $H_{\infty}$ performance is converted into the stabilization one for the error dynamics between the given nonlinear underwater glider and the reference time-varying input. Sufficient conditions are derived for the asymptotic stabilizability of the error dynamics in the format of matrix inequality. Simulation results demonstrate the effectiveness of the proposed design methodology.

• The Transactions of the Korean Institute of Electrical Engineers
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• v.40 no.9
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• pp.929-936
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• 1991

An estimator for a discrete nonlinear system is derived in the sense of minimum mean square error. An optimal estimator for nonlinear system is very difficult to find and it will be infinite dimensional even if it is found. It has been known that the statistical linearization technique makes it possible to obtain a finite dimensional estimator. In this paper, the procedure of its derivation using the statistical linearization technique that gives an exact mean and variance information is introduced in the sense of minimum mean square error. The derived estimator cannot be clainmed to be globally optimal estimator because it uses the Gaussian assumption to the non-Gaussian distributed nonlinear output. However, the proposed filter exhibits a better performance compared to extended Kalman filter. Simulation results of a simple example present the improvement of the proposed filter in convergent property over the extended Kalman filter.

• Proceedings of the KIEE Conference
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• 1994.11a
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• pp.356-358
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• 1994

In this paper, to alleviate the effect of approximation error and discontinuous variation of the controller parameters, the variable structure control scheme using neural networks is presented. In the proposed method, the variable structure control rules for each local linear models are designed to reject the effect of linearization error caused by linearization of the nonlinear system. And neural network infer approximate controller gains from combination of local linear control gains. The proposed control methods can be used to control nonlinear systems and it has robust characteristic against system parameter variations and external disturbances.

• Proceedings of the Korea Electromagnetic Engineering Society Conference
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• 2003.11a
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• pp.108-112
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• 2003

A feedforward amplifier, which is composed of several components, is an open loop system. Therefore, feedforward amplifiers are apt to deteriorate the performance according to the environmental changes even though the cancellation performance and the linearization bandwidth of feedforward systems are superior to other linearization methods. A control method is needed for maintaining the original performance of feedforward amplifiers or to keep the performance within a little error bounds. In this paper, an adaptive control method, which has a good convergence characteristic and is easy to implement, is suggested. The characteristics of the suggested control method compare with the characteristics of other control methods and the simulation results are presented.

• Structural Engineering and Mechanics
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• v.13 no.5
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• pp.569-589
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• 2002

In performing the dynamic analysis, the step size used in a step-by-step integration method might be much smaller than that required by the accuracy consideration in order to capture the rapid chances of dynamic loading or to eliminate the linearization errors. It was first found by Chen and Robinson that these difficulties might be overcome by integrating the equations of motion with respect to time once. A further study of this technique is conducted herein. This include the theoretical evaluation and comparison of the capability to capture the rapid changes of dynamic loading if using the constant average acceleration method and its integral form and the exploration of the superiority of the time integration to reduce the linearization error. In addition, its advantage in the solution of the impact problems or the wave propagation problems is also numerically demonstrated. It seems that this time integration technique can be applicable to all the currently available direct integration methods.

• The Journal of Korea Robotics Society
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• v.15 no.3
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• pp.248-255
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• 2020

This paper introduces a Feedback Linearization (FL) controller to eliminate the gyro effect on a quadrotor UAV. In order to control the attitude of the quadrotor, the second model equation was differentiated to the 4-th order to induce the control input to be revealed, and then a new control input was derived based on the attitude transformation equation with a gyro effect. For the initial quick posture control of the quadrotor, the existing yaw control was replaced with a separate controller. The simulation was conducted with an experiment in which FL control to remove the gyro effect was applied to the quadrotor and an experiment without removing the gyro effect, from the experimental results, the maximum error seen in each axial direction of the quadrotor was x = 0.22 m, y = 0.20 m, z = 0.16 m. Through the proposed method, the effect of the FL controller for controlling the gyro effect of the quadrotor was confirmed.

• Journal of the Korean Society for Precision Engineering
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• v.12 no.6
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• pp.145-151
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• 1995

The first-order Taylor series expansion can be evaluated analytically from the formulated symbolic nonlinear dynamic equations. A closed-form linear dynamic euation is derived about a nominal trajectory. The state space representation of the linearized dynamics can be derived easily from the closed-form linear dynamic equations. But manual symbolic expansion of dynamic equations and linearization is tedious, time-consuming and error-prone. So it is desirable to manipulate the procedures using a computer. In this paper, the analytic linearization is performed using the symbolic language MATHEMATICA. Two examples are given to illustrate the approach anbd to compare nonlinear model with linear model.

• Transactions of the Korean Society of Mechanical Engineers
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• v.14 no.5
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• pp.1104-1111
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• 1990

A method to identify nonlinear elements position of a nonlinear system is presented. Nonlinear elements position can be identified by an equivalent error damping and stiffness matrices which are based on the equivalent linearization technique. The procedures of this technique are: (1) Obtain input force and system response. (2) Define error between the actual and linearized restoring forces. (3) Calculate linearized damping and stiffness coefficients to minimize the square error sum. Several examples are tested and found that these methods are very effective not only to locate the nonlinear elements position but also to identify the degree of nonlinearity qualitatively. Nonlinear type can be qualitatively identified by examining the plots of restoring force vs equivalent state values.

• 제어로봇시스템학회:학술대회논문집
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• 1997.10a
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• pp.1419-1422
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• 1997

This paper deals with the Appliation of robust fault detection problem in uncertain linear systems, having both model mismatch and noise. A robust fault detection method presented by Kwon et al.(1994) for SISO uncertain systems. Here we experimented this method to the diesel engine systems described by difference ARMA models. The model mismatch includes here linearization error as well as undermodeling. Comparisons are made with alternative fault detection method which do not account noise. The new method is shown to have good performance.

• 제어로봇시스템학회:학술대회논문집
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• 1996.10b
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• pp.710-713
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• 1996

This paper deals with the fault detection problem in uncertain linear multivariable systems having both model mismatch and noise. A robust detection presented by Kwon et al.(1994) for SISO systems has been here extended to the multivariable systems are derived. The model mismatch includes here linearization error as well as undermodelling. Comparisons are made with alternative fault detection method which do not account noise. The new method is shown to have good performance.