• The KSFM Journal of Fluid Machinery
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• v.14 no.2
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• pp.29-34
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• 2011

Aerodynamic torque of wind turbine is highly nonlinear due to the nonlinear interactions between wind and blade. The aerodynamic nonlinearity is represented by nonlinear power and torque coefficients which are functions of wind speed, rotational speed of rotor, and pitch angle of blade. It is essential from the viewpoint of understanding and analysis of dynamic characteristics for wind turbine to linearize the aerodynamic torque and define aerodynamic nonlinear parameters as derivatives of aerodynamic torque with respect to the three parameters. In this paper, a linearization method of the aerodynamic torque from power coefficient is presented through differentiating it by the three parameters. And steady-state values of three aerodynamic nonlinear parameters according to wind speed are obtained and their nonlinear characteristics are investigated.

• Proceedings of the KIEE Conference
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• 2001.11c
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• pp.18-21
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• 2001

In this paper, some geometric condition for a stochastic nonlinear system and an adaptive control method for minimum-phase stochastic nonlinear system using neural network me provided. The state feedback linearization is widely used technique for excluding nonlinear terms in nonlinear system. However, in the stochastic environment, even if the minimum phase linear system derived by the feedback linearization is not sufficient to be controlled robustly. In the viewpoint of that, it is necessary to make an additional condition for observation of nonlinear stochastic system, called perfect filtering condition. In addition, on the above stochastic nonlinear observation condition, I propose an adaptive control law using neural network. Computer simulation shoo's that the stochastic nonlinear system satisfying perfect filtering condition is controllable and the proposed neural adaptive controller is more efficient than the conventional adaptive controller.

• 제어로봇시스템학회:학술대회논문집
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• 1991.10a
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• pp.1116-1121
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• 1991

In this paper the feedback linearization of the valve-controlled nonlinear hydraulic velocity control system and the Implementation of the digital state feedback controller is studied. The C.inf. nonlinear transformation to the electro-hydraulic velocity control system, which transforms nonlinear system to linear equivalent one, is obtained. It is shown that this transformation Is global one. The digital controller to this linearized model is obtained by using the one-step ahead state estimator and implemented to real plant. The proposed method In this paper is easier to implement than other proposed methods and it is possible to control in real tine. The experiment and simulation study show that the implementation of the digital state feedback controller based on the feedback linearized model is successful.

• Journal of Institute of Control, Robotics and Systems
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• v.16 no.8
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• pp.727-734
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• 2010

This paper deals with an anti-sway control for a ship-mounted container crane which is disturbed by the wave-induced motions of the ship. We derive a simple dynamics of the ship-mounted container crane with an active anti-sway control system and transform it into a dynamic function for a horizontal variable on the absolute coordinate. Then we propose an control method to reduce pendulation of the spreader and compare its performance with well-known feedback linearization control in computer simulation.

• 제어로봇시스템학회:학술대회논문집
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• 2000.10a
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• pp.18-18
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• 2000

In this paper, some geometric condition for a stochastic nonlinear system and an adaptive control method for minimum-phase stochastic nonlinear system using neural network are provided. The state feedback linearization is widely used technique for excluding nonlinear terms in nonlinear system. However, in the stochastic environment, even if the minimum phase linear system derived by the feedback linearization is not sufficient to be controlled robustly. the viewpoint of that, it is necessary to make an additional condition for observation of nonlinear stochastic system, called perfect filtering condition. In addition, on the above stochastic nonlinear observation condition, I propose an adaptive control law using neural network. Computer simulation shows that the stochastic nonlinear system satisfying perfect filtering condition is controllable and the proposed neural adaptive controller is more efficient than the conventional adaptive controller

• Transactions of the Korean Society for Noise and Vibration Engineering
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• v.23 no.5
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• pp.445-455
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• 2013

A new linearization model for MR dampers is analyzed. The nonlinear hysteretic damping force model of MR damper can be modeled as a hyperbolic tangent function of currents, positions, and velicities, which is an algebraic function with constant parameters. Model parameters can be identified with numerical method using experimental force-velocity-position data obtained from various operating conditions. The nonlinear hysteretic damping force can be linearized with a given slope of damping coefficient if there exist corresponding currents to compensate for the nonlinearity. The corresponding currents can be calculated from the inverse model when the given linear damping force is set equal to the nonlinear hysteretic damping force. The linearization controller is realized in a DSP controller such that the corresponding currents to satisfy a given damping coefficient should be calculated. Experiments show that the current inputs to the MR damper produce linearized damping force with a given slope of the damping coefficient.

• Journal of Electrical Engineering and information Science
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• v.2 no.4
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• pp.28-36
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• 1997

In this paper, robust stability analysis for the fuzzy feedback linearization regulator is presented. Well-known Takagi-Sugeno fuzzy model is used as the MISO nonlinear plant model. Uncertainty and disturbance are assumed to be included in the model structure with known bounds. For these structured uncertainty and disturbances, robust stability of the close system is analyzed in both input-output sense and Lyapunov sense. The robust stability conditions are proposed by using multivariable circle criterion and the relationship between input-output stability and Lyapunov stability. The proposed stability analysis is illustrated by a simple example.

• International Journal of Control, Automation, and Systems
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• v.4 no.3
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• pp.302-307
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• 2006

The aim of applying Functional Electrical Stimulation (FES) is to restore a person's motor function by directly supplying the controlled electrical currents to the site of the paralyzed muscles. However, most clinically utilized FES systems have adapted an open-loop control scheme. Recently the closed-loop control scheme has been considered for setting up the FES system, but due to the inherent nonlinearities in the musculoskeletal system, the nonlinearities were not fully compensated and it caused the oscillatory responses for tracking the output variables. In this study, a nonlinear controller model that has two inverse compensation units is proposed with the compromising feedback linearization method and this will eventually be used to design the FES control system for stimulating a knee joint musculoskeletal system.

• The KIPS Transactions:PartA
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• v.8A no.1
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• pp.56-62
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• 2001

This paper presents a neural network based variable structure control scheme for nonlinear systems. In this scheme, a set of local variable structure control laws are designed on the basis of the linear models about preselected representative points which cover the range of the system operation of interest. From the combination of the set of local variable structure control laws, neural networks infer the approximate control input in between the operating points. The neural network based variable structure control alleviates the effects of model uncertainties, which cannot be compensated by the control techniques using feedback linearization. It also relaxes the discontinuity in the system’s behavior that appears when the control schemes based on the family of the linear models are applied to nonlinear systems. Simulation results of a ball and beam system, to which feedback linearization cannot be applied, demonstrate the feasibility of the proposed method.

• Journal of the Korean Institute of Telematics and Electronics S
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• v.34S no.3
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• pp.46-54
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• 1997

In this paper, we psropose the feedback linearization technique for a nonlinear system using genetic algorithms (GAs) and fuzzy logic system. The proposed control scheme approximates the nonlinear term of a nonlinear system using the fuzzy logic system and computes the control input for cancelling the nonlinear term. Then in the fuzzy logic system, the number and shape of membership function of the premise aprt will be tuned to minimize the control error boundary using GAs. And the parameters of the consequence of fuzzy rule will be tuned by the adaptive laws based on lyapunov stability theory in order to guarantee the closed loop stability of control system. The evolution of fuzzy logic system is processed during the on-line adaptive control. The effectiveness of proposed method will be demonstrated by computer simulation of simple nonlinear sytem.