• 한국신재생에너지학회:학술대회논문집
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• 2011.11a
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• pp.40.1-40.1
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• 2011

This paper presents a pitch controller which can hold output power constant at the rated value. Although wind turbine contains complicated nonlinearities, its behaviour within a certain operating range of a point can be approximated by that of a linear model. By doing so, we can apply rather simple and systematic linear control techniques such as PID and LQR(Linear Quadratic Regulator) to design a linear pitch controller. Because these linear controllers are valid only in a sufficiently small range around an operating point, linearized wind turbine model under the condition of varying wind speed needs a linear pitch controller can achieve the aims of tracking the rated rotor rotational speed. We propose an improved linear pitch controller taking each merit of LQR and PI controller under the condition of varying operating points in this paper.

• 제어로봇시스템학회:학술대회논문집
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• 2001.10a
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• pp.75.2-75
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• 2001

In this paper, we provide the technique of H$\infty$ controller design algorithm for input-saturated linear systems using a linear parameter varying(LPV) framework. The LPV controller with parameter dependent dynamic state feedback controller concept guarantees the asymtotic stability and H$\infty$ norm bound within prescribed level v using the saturation nonlinearity as scheduling parameters. Especially, the sufficient conditions for the existence of H$\infty$ controller are formulated in terms of linear matrix inequalities(LMIs) that can be solved very efficiently.

• Journal of the Semiconductor & Display Technology
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• v.7 no.1
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• pp.17-22
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• 2008

The proposed hybrid controller consists of PID controller, feedforward controller and RLSE (Recursive Least Square Estimating) adaptive controller to compensate the force ripple that is periodic function of position in a linear motor. The modeling of force ripple is divided into the current-dependent and current-independent components. The current independent components never change as the current into the linear motor changes. On the other hand, the current-dependent components change as current varies when the velocity and load of the linear motor change. The proposed controller can compensate both force ripples. The feedforward controller compensates the current-independent components and the RLSE adaptive controller compensates the current-dependents components. We verified the performance of the controller by simulation and experiments.

• Smart Structures and Systems
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• v.9 no.4
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• pp.373-392
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• 2012

Tracking control of systems with variable stiffness hysteresis using a gain-scheduled (GS) controller is developed in this paper. Variable stiffness hysteretic system is represented as quasi linear parameter dependent system with known bounds on parameters. Assuming that the parameters can be measured or estimated in real-time, a GS controller that ensures the performance and the stability of the closed-loop system over the entire range of parameter variation is designed. The proposed method is implemented on a spring-mass system which consists of a semi-active independently variable stiffness (SAIVS) device that exhibits hysteresis and precisely controllable stiffness change in real-time. The SAIVS system with variable stiffness hysteresis is represented as quasi linear parameter varying (LPV) system with two parameters: linear time-varying stiffness (parameter with slow variation rate) and stiffness of the friction-hysteresis (parameter with high variation rate). The proposed LPV-GS controller can accommodate both slow and fast varying parameter, which was not possible with the controllers proposed in the prior studies. Effectiveness of the proposed controller is demonstrated by comparing the results with a fixed robust $\mathcal{H}_{\infty}$ controller that assumes the parameter variation as an uncertainty. Superior performance of the LPV-GS over the robust $\mathcal{H}_{\infty}$ controller is demonstrated for varying stiffness hysteresis of SAIVS device and for different ranges of tracking displacements. The LPV-GS controller is capable of adapting to any parameter changes whereas the $\mathcal{H}_{\infty}$ controller is effective only when the system parameters are in the vicinity of the nominal plant parameters for which the controller is designed. The robust $\mathcal{H}_{\infty}$ controller becomes unstable under large parameter variations but the LPV-GS will ensure stability and guarantee the desired closed-loop performance.

• 제어로봇시스템학회:학술대회논문집
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• 1989.10a
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• pp.402-406
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• 1989

Since the control parameters of classical PID controller are fixed for all control period, it is not easy to produce a desired transition phenomena. We incorporate an iterative learning scheme to the linear controller so that it has more flexibility and adaptation capability especially in the transition period. In this paper a hybrid type learning controller is proposed in which fixed linear controller guides learning at the beginning stage. Once a perfect learning is achieved, then the control action is performed by only the learning controller. A computer simulation result demonstrates better performance during transition time than that with only linear PD controller.

• Journal of Advanced Marine Engineering and Technology
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• v.15 no.1
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• pp.85-95
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• 1991

All physical systems are nonlinear to some degree. The examples are relay, backlash, deadzone, saturation element and so on. In the linear control system design, it is useful method to restrict the nonlinearity to the linearity of system over the operation range. It is worth noting that nonlinearities may be intentionally introduced in to a system. A simple of an intentional non-linearity is the Bang-Bang controller which uses the On-Off relay. In this paper, an angular position servosystem made of a DC servomotor controlled by a microcomputer is discribed. Authors use two methods in the design of controller. The one is linear controller designed by the optimal feedback control theory only and the other is nonlinear controller designed by On-Off relay with optimal feedback control theory. To do the real time control, the controller is designed by using 16bit personal computer and A/D.D/A converter(12bit) is used in order to convert the signal. According to this way, the results from real time control are as follows. 2) Under the On-Off controller with hysterisis the influence of disturbance is considerably smaller than the linerar controller. 3) An increase in the sampling period has a destabilizing effect. 4)In the controller performance, the response time of the On-Off controller is longer than that of the linear controller. To close, we note that the On-Off controller with hysterisis is more attractive than the linear controller in the presence of the input limit.

• Journal of Navigation and Port Research
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• v.34 no.2
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• pp.117-122
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• 2010

Since fuzzy controllers are nonlinear, it is more difficult to set the controller gains and to analyse the stability compared to conventional PID controllers. This paper proposes a fuzzy PD+I controller for tracking control which uses a linear fuzzy inference(product-sum-gravity) method based on a conventional linear PID controller. In this scheme the fuzzy PD+I controller works similar to the control performance as the linear PD plus I(PD+I) controller. Thus it is possible to analyse and design an fuzzy PD+I controller for given systems based on a linear fuzzy PD controller. The scaling factors tuning scheme, another topic of fuzzy controller design procedure, is also introduced in order to fine performance of the fuzzy PD+I controller. The scaling factors are adjusted by a real-coded genetic algorithm(RCGA) in off-line. The simulation results show the effectiveness of the proposed fuzzy PD+I controller for tracking control problems by comparing with the conventional PID controllers.

• 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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• 1993.07a
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• pp.51-53
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• 1993

This paper proposes a design of the tracking controller using artificial neural network and the compare the result with a result of optimal controller. In practical use, conventional Optimal controller has some limits. First, optimal controller can be designed only for linear system. Second, for many systems state observation is difficult or sometimes impossible. But the controller using artificial neural network does not need mathmatical model of the system including state observation, so it can be used for both linear and nonlinear system with no additional cost for nonlinearity. Designed multi layer neural network controller is composed of two parts, feedforward controller gives a steady state input & feedback controller gives transient input via minimizing the quadratic cost function. From the comparison of the results of the simulation of linear & nonlinear plant, the plant controlled by using neural network controller shows the trajectory similar to that of the plant controlled by an optimal controller.

• Journal of the Korean Institute of Telematics and Electronics B
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• v.33B no.5
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• pp.1-9
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• 1996

In this paper, a parameter identification method for a discrete-time linear system using multi-layer neural network is proposed. The parameters are identified with the combination of weights and the output of neuraons of a neural network, which can be used for a linear and a nonlinear controller. An adaptive output tracking architecture is designed for the linear controller. And, the nonlinear controller. A sliding mode control law is applied to the stabilizing the nonlinear controller such that output errors can be reduced. The effectiveness of the proposed control scheme is illustrated through simulations.