• Proceedings of the KIEE Conference
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• 1999.07b
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• pp.936-938
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• 1999

In this Paper, the design of a feedback linearization controller using multilayer neural network is proposed. The Proposed feedback linearization control scheme is designed by finding Lie derivatives from an identified neural networks. Lie derivatives are expressed as a combination of weights and neuron outputs. The proposed method is applied to an antenna arm problem and the simulation results show performance comparisons between the ordinary feedback linearization and the Proposed method.

• 제어로봇시스템학회:학술대회논문집
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• 2000.10a
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• pp.202-202
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• 2000

In this paper, the synchronous generator model which is derived from the swing equation and the Park's equation is transformed to an adequate form for feedback linearization. And the single controller is designed in order to play the role of both PSS and AVR. The result of the simulations shows that the stability of the system is guaranteed in the wide range of operating points.

• The Transactions of the Korean Institute of Power Electronics
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• v.18 no.4
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• pp.333-341
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• 2013

In this paper, a feedback linearization control is proposed to regulate the output voltages of a three-phase four-wire inverter under the unbalanced and nonlinear load conditions. First, the nonlinear model of system including the output LC filters is derived in the d-q-0 synchronous reference frame. Then, the system is linearized by the multi-input multi-output feedback linearization. The tracking controllers for d-q-0-components of three-phase line-to-neutral load voltages are designed by linear control theory. The experimental results have shown that the proposed control method gives the good performance in response to the load conditions.

• Transactions on Control, Automation and Systems Engineering
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• v.4 no.4
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• pp.356-362
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• 2002

In this paper, well-known Takagi-Sugeno fuzzy model is used as the nonlinear plant model and uncertainty is assumed to be included in the model structure with known bounds. Based on the fuzzy models, a numerical robust stability analysis for the fuzzy feedback linearization regulator is presented using Linear Matrix Inequalities (LMI) Theory. For these structured uncertainty, the closed system can be cast into Lur'e system by simple transformation. From the LMI stability condition for Lur'e system, we can derive the robust stability condition for the fuzzy feedback linearization regulator based on Takagi-Sugeno fuzzy model. The effectiveness of the proposed analysis is illustrated by a simple example.

• Journal of the Korean Society for Precision Engineering
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• v.24 no.4 s.193
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• pp.68-75
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• 2007

In this study, the ultimate goal is to acquire stability when turning around efficiently by using the controller which is applied partial feedback linearization of One-wheel Unicycle Robot. When moving around, linear controller could result in unstable factor according to widening operation range. So in order to reduce instability, 1 have developed Non-linear Controller using Partial Feedback Linearization. Compared with linear controller, Non-linear Controller guarantees the superiority of Regulating Control and Tracking Control in direct and also revolution motion of Robot. I'm sure of the Non-linear controller performance through many experiments.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.64 no.2
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• pp.297-303
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• 2015

We propose a disturbance observer(DOB) based feedback linearization control to improve position tracking performance in the presence of disturbance. The proposed method consists of a disturbance observer and a feedback linearization controller. The disturbance observer is designed to estimate the load force disturbance in electro-hydraulic systems. An auxiliary state variable is proposed in order to avoid amplification of the measurement noises in the disturbance observer. Using the estimated disturbance enables the Electro-hydraulic servo systems(EHS) dynamics to be changed into feedback linearization from. In order to compensate for the disturbance and to track the desired position, the feedback linearization based controller is proposed. The proposed method has a simple structure which can easily be implemented in practice. As a result, the proposed method improves the position tracking performance in the presence of disturbance. Its performance is validated via simulations.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 1997.11a
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• pp.185-188
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• 1997

In this paper, we execute identification, linearization, and control of a nonlinear system using recurrent neural networks. In general nonlinear control system become complex because of nonlinearity and uncertainty. And though we compose nonlinear control system based on the model, it is difficult to get good control ability. So we identify the nonlinear control system using the recurrent neural networks and execute feedback linearization of identified model, In this process we choose the optional linear system, and the system which will have to be feedback linearized if trained to follow the linearity between input and output of the system we choose. We the feedback linearized system by applying standard linear control strategy and simulation. And we evaluate the effectiveness by comparing the result which is linearized theoretically.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.66 no.5
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• pp.819-832
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• 2017

We propose a new switching control scheme for a ball and beam system by utilizing two linearization methods. First, the Jacobian linearization is applied and state observer is developed afterward. Then, motivated [6], the approximate input-output linearization is carried out, and after that, the Jacobian linearization is applied along with the design of state observer. Since the second approach requires two linearizations, it is called a two-step linearization method. The state observer is needed for the estimation of the velocities of ball and motor movement. Since the Jacobian linearization based controller tends to provide faster response at the initial time, and after that, the two-step linearization based controller tends to provide better response in terms of output overshoot and convergence to the origin, it is natural to give a switching control scheme to provide the best overall control response. The validity of our control scheme is shown in both simulation and experimental results.

• 제어로봇시스템학회:학술대회논문집
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• 1996.10b
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• pp.1064-1067
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• 1996

This paper describes the design and implementation of an intelligent controller for continuous casting process. The proposed controller adopted a fuzzy control with feedback linearization. The simulation result shows that proposed intelligent controller is superior to the conventional PID controller.

• 제어로봇시스템학회:학술대회논문집
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• 1997.10a
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• pp.644-647
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• 1997

Asymmetric cylinders are usually used as an actuator of active suspensions. The conventional optimal controller design does not include actuator dynamics as a state and force controller is needed to track the desired force. But the actuator is not ideal, so performance of an active suspension system is degraded. In this paper, we take account nonlinear actuator dynamics and obtain a linear model using a feedback linearization technique then apply optimal control method. Effectiveness of proposed method is demonstrated by numerical simulation of 1/4 car model.