• 대한기계학회:학술대회논문집
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• 대한기계학회 2001년도 춘계학술대회논문집B
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• pp.694-699
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• 2001

This paper presents a control of active suspension system for quarter-car model with two-degree-of-freedom using $H_{inf}$ and nonlinear adaptive robust control method. Suspension dynamics is linear and treated by $H_{inf}$ method which guarantees the robustness of closed loop system under the presence of uncertainties and minimizes the effect of road disturbance to system. An Adaptive Robust Control (ARC) technique is used to design a force controller such that it is robust against actuator uncertainties. Simulation results are given for both frequency and time domains to verify the effectiveness of the designed controllers.

• 한국정밀공학회지
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• 제18권9호
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• pp.71-81
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• 2001

This paper analyzes the effectiveness of minimizing vibration and sound transmission on/through a thin rectangular plate by both feedback control and hybrid control which combines adaptive feedforward control with a feedback loop. An experimental system identification technique using the matrix-fractional curve-fitting of the frequency response data is introduced for complex shaped structures. This identification technique reduces the model order o the MIMO(Multi-Input Multi-Output) system which simplifies the practical implementation. The adaptive feedforward control uses a Multiple filtered-x LMS(Least Mean Square) algorithm and the feedback control uses a multivariable digital LQG(Linear Quadratic Gaussian) algorithm. Experimental results show that an effective reduction of sound transmission is achieved by the hybrid control scheme when both vibration and noise measurement signals are incorporated in the controller.

• 제어로봇시스템학회:학술대회논문집
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• 제어로봇시스템학회 1988년도 한국자동제어학술회의논문집(국내학술편); 한국전력공사연수원, 서울; 21-22 Oct. 1988
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• pp.187-191
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• 1988

To track the manenvering target and to derive the Filter using state estimation and information in real time, we derive adaptive Kalman Filter which reinitialize the internal filter mode.

• 전력전자학회:학술대회논문집
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• 전력전자학회 1996년도 창립기념 전력전자학술발표회 논문집
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• pp.17-20
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• 1996

An adaptive current control scheme of a permanent magnet (PM) synchronous motor with the simultaneous estimation of the magnitude of the flux linkage and stator resistance is proposed. The adaptive parameter estimation is achieved by a model reference adaptive system (MRAS) technique. The adaptive laws are derived by the Popov's hyperstability theory and the positivity concept. The predictive control scheme is employed for the current controller with the estimated parameters. The robustness of the proposed current control scheme is compared with the conventional one through the computer simulations.

• 제어로봇시스템학회:학술대회논문집
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• 제어로봇시스템학회 2000년도 제15차 학술회의논문집
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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

• 대한전기학회논문지
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• 제45권4호
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• pp.582-589
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• 1996

In this paper, an adaptive control problem of a hydraulic actuator for vehicle active suspension controller is divided into two parts: the inner loop controller and the outer loop controller. Inner loop controller, which is a nonlinear adaptive controller, is designed to control the force generated by the nonlinear hydraulic actuator acting under the effects of Coulomb friction. For simplicity of designing a nonlinear controller, the spool valve dynamics of a hydraulic actuator is reduced using a singular perturbation technique. The estimation error signal used to an indirect parameter adaptation is calculated without a regressor filtering. The absolute velocity of a sprung mass will be damped down by its negatively proportional term(sky-hook damper) adopted as an outer loop controller. Simulation results are presented to show the importance of controlling the actuator force and the validity of the proposed adaptive controller. (author). refs., figs. tab.

• 한국항해항만학회:학술대회논문집
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• 한국항해항만학회 2006년도 춘계학술대회 및 창립 30주년 심포지엄(논문집)
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• pp.67-74
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• 2006

In this paper, an adaptive neural network controller and its application to automatic berthing control of ship is presented. The neural network controller is trained online using adaptive interaction technique without any teaching data and off-line training phase. Firstly, the neural networks used to control rudder and propeller during automatic berthing process are presented. Finally, computer simulations of automatic ship berthing are carried out to verify the proposed controller with and without the influence of wind disturbance and measurement noise.

• 대한조선학회논문집
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• 제31권2호
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• pp.38-44
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• 1994

1960년대 Auto-Pilot 시스템이 대양을 항해하는 선박에 장착되면서 운항경제성 및 선원 승선감의 견지에서 Auto-Pilot시스템의 제어 알고리듬이 연구되기 시작하였다. 초기에는 PD제어로 시작하였으나 잦은 타동작으로 인한 기구 마모를 줄이기 위해 실제 선수각과 설정선수방위 차가 어느 상한치 이상에서만 타각을 조작하는 on-off 제어를 도입하였다. 본 논문에서는 Auto-Pilot 시스템의 제어 알고리듬에 optimal제어, adaptive제어 등을 적용하여 제어기법간의 비교를 cost function을 통하여 수행함으로써 Auto-Pilot 시스템에 대한 최적의 제어기법을 조사하였다. Adaptive 제어를 위한 선박 조종운동방정식의 parameterization 과정을 검토하였고 adaptive 알고리듬의 장점이 파라미터 추정이 잘못된 경우의 수치시뮬레이션 결과로부터 분명히 알 수 있었다.

• 제어로봇시스템학회:학술대회논문집
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• 제어로봇시스템학회 2005년도 ICCAS
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• pp.1478-1481
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• 2005

As the model of most practical system cannot be obtained, the practice of typical control method is limited. Accordingly, numerous artificial intelligence control methods have been used widely. Fuzzy control and neural network control have been an important point in the developing process of the field. This paper is proposed adaptive fuzzy-neural network based on the vector controlled interior permanent magnet synchronous motor drive system. The fuzzy-neural network is first utilized for the speed control. A model reference adaptive scheme is then proposed in which the adaptation mechanism is executed using fuzzy-neural network. Also, this paper is proposed estimation of speed of interior permanent magnet synchronous motor using artificial neural network controller. The back-propagation neural network technique is used to provide a real time adaptive estimation of the motor speed. The error between the desired state variable and the actual one is back-propagated to adjust the rotor speed, so that the actual state variable will coincide with the desired one. The back-propagation mechanism is easy to derive and the estimated speed tracks precisely the actual motor speed. This paper is proposed the analysis results to verify the effectiveness of the new method.

• 제어로봇시스템학회논문지
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• 제16권1호
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• pp.5-11
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• 2010

An adaptive formation control approach is proposed for nonhonolomic multiple mobile robots considering unknown slipping and skidding. It is assumed that unknown slipping and skidding effects are bounded by unknown constants. Under this assumption, the adaptive technique is employed to estimate the bounds of unknown slipping and skidding effects of each mobile robot. To deal with the skidding effect included in kinematics, the dynamic surface design approach is applied to design a local controller for each mobile robot. Using Lyapunov stability theorem, the adaptation laws for tuning bounds of slipping and skidding are induced and it is proved that all signals of the closed-loop system are bounded and the tracking errors and the synchronization errors of the path parameters converge to an adjustable neighborhood of the origin. Finally, simulation results are provided to verify the effectiveness of the proposed approach.