• Proceedings of the KIEE Conference
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• 1993.07a
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• pp.268-270
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• 1993

The implementation of Nonlinear Feedback Linearization control for Electro-Magnetic Suspension system is presented. The controller using TMS320C31 DSP chip was proposed and the experiments were performed Control law for EMS system using feedback linearization is derived and implemented in the DSP. Some tests were constructed far experimental comparison between feedback linearization and classical state feedback The experimental results demonstrate that the feedback linearization controller shows bettor performance than that of the classical state feedback controller and it is robust with respect to disturbance and parameter variation, though some steady-state errors appear.

• Journal of Power Electronics
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• v.19 no.2
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• pp.569-579
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• 2019

A robust controller is designed based on feedback linearization and sliding mode control to damp sub-synchronous control interaction (SSCI) in doubly fed induction generator (DFIG) wind power plants (WPPs) interfaced with the grid. A feedback-linearized sliding mode controller (FLSMC) is developed for the rotor-side converter (RSC) through feedback linearization, design of the sliding mode controller, and parameter tuning with the use of particle swarm optimization. A series-compensated 100-MW DFIG WPP is adopted in simulation to evaluate the effectiveness of the designed FLSMC at different compensation degrees and wind speeds. The performance of the designed controller in damping SSCI is compared with proportional-integral controller and conventional sub-synchronous resonance damping controller. Besides the better damping capability, the proposed FLSMC enhances robustness of the system under parameter variations.

• 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.

• 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.

• 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.

• Transactions on Control, Automation and Systems Engineering
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• v.2 no.4
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• pp.221-227
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• 2000

This paper combines the constrained model predictive control with the feedback linearization to solve a nonlinear system control problem with input constraints. The combined approach consists of two steps: Firstly, the nonlinear model is linearized by the feedback linearization. Secondly, based on the linearized model, the constrained model predictive controller is designed taking input constraints into consideration. The proposed controller is applied to two link robot system, and tracking performances of the controller are investigated via some simulations, where the comparisons are done for the cases of unconstrained, constrained input in feedback linearization.

• Journal of Power Electronics
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• v.9 no.2
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• pp.288-299
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• 2009

This paper proposes a feedback linearization control scheme of AC/DC PWM converters with LCL input filters using no damping resisters. Feedback linearization techniques use a transformation from nonlinear system models into equivalent linear models in a simpler form. The feedback linearization scheme in this work has cascade structures unlike usual feedback linearization, therefore it has an advantage that it is possible to limit the capacitor current to a certain level. The performance of the proposed controller is validated with 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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• 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.

• Journal of the Korean Society for Aeronautical & Space Sciences
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• v.36 no.6
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• pp.558-564
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• 2008

This paper presents an approach to combine adaptive controller with feedback linearization, which extends the applicability of the adaptive controller to a wider class of systems. The adaptive controller guarantees the asymptotic tracking convergence and the transient performance of the tracking error. The feedback linearization transforms a nonlinear plant into a linear time invariant form. The asymptotic tracking convergence is shown by the use of Lyapunov stability analysis and Barbalat's lemma.