• 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.

• Proceedings of the Korean Society of Machine Tool Engineers Conference
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• 1999.10a
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• pp.216-220
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• 1999

In this paper, the feedback linearization technique is used with the sliding mode control for nonlinear system. The combination of these two control techniques can be achieved by proposing a novel sliding surface which has the nonminal dynamics of the original system controlled by feedback linearization technique. The noble design of the sliding surface is based on the augmented system whose dynamics have a higher order than that of the original system. The reaching phase is removed by using an initial virtual state which makes the initial sliding function equal to zero

• International Journal of Control, Automation, and Systems
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• v.5 no.4
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• pp.379-387
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• 2007

In this paper, a nonlinear anti-sway controller for container cranes with load hoisting is investigated. The considered container crane involves a planar motion in conjunction with a hoisting motion. The control inputs are two (trolley and hoisting forces), whereas the variables to be controlled are three (trolley position, hoisting rope length, and sway angle). A novel feedback linearization control law provides a simultaneous trolley-position regulation, sway suppression, and load hoisting control. The performance of the closed loop system is shown to be satisfactory in the presence of disturbances at the payload and rope length variations. The advantage of the proposed control law lies in the full incorporation of the nonlinear dynamics by partial feedback linearization. The uniform asymptotic stability of the closed-loop system is assured irrespective of variations of the rope length. Simulation and experimental results are compared and discussed.

• 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.

• 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.

• Journal of Institute of Control, Robotics and Systems
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• v.6 no.2
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• pp.127-137
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• 2000

This paper presents a novel neural based controller which controls the water level of the nuclear power plant steam generator. The controller consists of a model reference feedback linearization controller and a PI controller for stabilizing the feedback linearization controller. The feedback linearization controller consists of a neural network model and an inversing module which uses the neural network model for computing the control input to the steam generator. We chose Piecewise Linearly Trained Network(PLTN) and Recurrent Neural Netwrok(RNN) for an approximator of the plant and used these approximators in calculating the input from the feedback linearization controller. Combining the above two controllers gives a result of better performance than the case which uses only a PI controller Each control result of PLTN and RNN is given.

• Transactions on Control, Automation and Systems Engineering
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• v.4 no.4
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• pp.253-263
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• 2002

This paper presents an adaptive feedback linearization control scheme for induction motors using stator fluxes. By using stator flukes as states, overparameterization is prevented and control inputs can be determined straightforwardly unlike in existing schemes. This approach leads to the decrease of the relative degree for the flux modulus and thus yields a simpler control algorithm than the prior results. In this paper. adaptation schemes are suggested to compensate for the variations of stator resistance. rotor resistance and load torque. In particular, the adaptation to the variation of stator resistance with a feedback linearization control is a new trial. In addition, to improve the convergence of rotor resistance estimation, the differences between stator currents and its estimates are used for the parameter adaptation. The simulations show that torque and flux are controlled independently and that the estimates of stator resistance, rotor resistance, and load torque converge to their true values. Actual experiments on a 3.7㎾ induction motor verify the effectiveness of the proposed method.

• 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.

• 제어로봇시스템학회:학술대회논문집
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• 2000.10a
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• pp.218-218
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• 2000

Advanced Vehicle Control Systems(AVCS) is one of the key elements in Intelligent Transportation Systems(ITS). This paper considers the problem of longitudinal control in vehicle platoon on a straight lane of a highway. In a very simplified situation, longitudinal vehicle dynamics contains many nonlinear elements. The nonlinear characteristics are mainly composed of an engine, a torque converter, and a drag force. In this paper, sliding control, one of nonlinear control methods, is applied to longitudinal automated vehicle control for platooning. Output feedback linearization is also simulated for comparison with the sliding control. Simulations for comparative study for the adopted controllers such as sliding control and output feedback linearization are peformed under the same conditions. This Paper aims at clarifying the characteristics of sliding control and output feedback linearization.

• 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.