• 제어로봇시스템학회:학술대회논문집
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• 2001.10a
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• pp.101.5-101
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• 2001

A linear input/output data-based predictive control with integral action is developed. The control input is obtained directly from the input/output data in a single step. However, the state estimation in subspace identification gives a biased estimate and there is model mismatch when the controller is applied to a nonlinear process. To overcome such difficulties, we add integral action to a linear input/output data-based predictive controller by augmenting the integrated white noise disturbance model and use each of best linear unbiased estimation(BLUE) filter and Kalman filter as a stochastic observer for the unmeasured disturbance. When applied to a continuous styrene polymerization reactor the proposed controller demonstrates.

• Journal of Institute of Control, Robotics and Systems
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• v.20 no.7
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• pp.695-700
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• 2014

In this paper, we utilize the CMG's momentum bias to control the roll/pitch attitude of the Quad-rotor. While the previous control approaches have used the thrust control approach, we design and add a new momentum controller (using CMG) in order to improve the transient response over the existing methods. The focal point of this paper is the design of a controller for a Quad-rotor's attitude using CMG. This leads to other tasks such as an identification of the model's parameters and mathematical nonlinear modeling. Then, the previous thrust controller is designed based on the linearized model. Finally, the overall system with our designed controller is implemented and tested in real time to show that the Quad-rotor is kept in a good balanced position faster than the traditional thrust-only control approach.

• Proceedings of the Korean Society of Marine Engineers Conference
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• 2005.11a
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• pp.216-217
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• 2005

This paper presents an integrated navagation control concept for underwater vehicles under high speed navigation circumstance. First of all, in order to control an underwater vehicle with respect to automatic navigation, an integrated navigation control method is suggested in view of synchronous control for course keeping, diving and depth control. An exact nonlinear model equation with six-degree-of-freedom is derived for control algorithm. To identify various hydrodynamic coefficients of the equation, an experimental approach is introduced and results are demonstrated for MANTA type model.

• Transactions of the Korean Society of Mechanical Engineers A
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• v.27 no.3
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• pp.472-477
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• 2003

A piezo-actuator is an important component for an E-beam lithography system. But it is very difficult to model its characteristics due to nonlinearities such as hysteresis and creep, to the input voltage. In this paper, one-axis micro stage with a piezo-actuator is modeled including the nonlinear properties. Hysteresis and creep are modeled as the first order differential equation and a time-dependent logarithmic function, respectively. The dynamic motion of the stage is also modeled as a mass-spring-damper system and the parameters are determined by utilizing the system identification technique. The simulation tool for a micro stage is constructed using the commercial software and its simulation results are compared with the experimental data.

• 제어로봇시스템학회:학술대회논문집
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• 1995.10a
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• pp.119-122
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• 1995

Neural network has found wide applications in the system identification, modeling, and realization based on its function approximation capability. THe system governe dby nonlinear dynamics is hard to be identified by the neural network because there exist following difficulties. FIrst, the training samples obtained by the stae trajectory are apt to be nonuniform over the region of interest. Second, the system may becomje unstable while attempting to obtain the samples. This paper deals with these problems in discrete-time system and suggest effective solutions which provide stability and uniform sampliing by the virtue of robust control theory and heuristic algorithms.

• Proceedings of the IEEK Conference
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• 1999.06a
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• pp.482-485
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• 1999

In this paper we proposed a heterogeneous hidden layer consisting of both sigmoid functions and RBFs(Radial Basis Function) in multi-layered neural networks. Focusing on the orthogonal relationship between the sigmoid function and its derivative, a derived RBF that is a derivative of the sigmoid function is used as the RBF in the neural network. so the proposed neural network is called ONN(Orthogonal Neural Network). Identification results using a nonlinear function confirm both the ONN＇s feasibility and characteristics by comparing with those obtained using a conventional neural network which has sigmoid function or RBF in hidden layer

• Proceedings of the Earthquake Engineering Society of Korea Conference
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• 2002.09a
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• pp.213-220
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• 2002

본 연구에서는 최근에 개발된 Unscented Particle Filter (UPF)를 사용한 비선형 동적 구조계의 구조계수 규명기법이 연구되었다. 일반적인 비선형 구조계수 추정 문제의 일반 해는 존재하지 않으나, 그에 대한 대안으로써 선형 근사 기법인 extended Kalman filter (EKF)가 비선형 동적 구조계수의 추정에 주로 사용되어왔다. 그러나, EKF는 구간 선형(piecewise linear) 가정으로 인해 biased estimator이고 비선형성이 상대적으로 높을 때 오차가 큰 추정치를 주는 단점을 가진다. 이를 보완하기 위해서 UPF가 개발되었고, 이 기법은 particle filter의 일종으로써 Unscented Kalman filter (UKF)를 사용하여 importance proposal distribution을 생성한다. 수치실험이 SDOF와 MDOF에 대하여 3가지 경우에 대해서 수행되었다. 비선형 SDOF의 수치 실험으로부터 잡음이 가해진 상태에서 UKF가 EKF에 비해 초기 공분산 행렬의 가정에 대해 정확하고 강인한 추정결과를 보여줌을 보였다 최하층의 column에 비선형 거동이 발생하는 5층 전단 빌딩모형의 수치실험으로부터 UKF가 복잡한 구조물의 구조계수 추정능력이 있음을 보여주었다. 여러 가지 수치실험은 UPF가 EKF보다 비선형 동적 구조계수 추정에 있어서 더 나은 방법임을 보여 주었다.

• Proceedings of the KIEE Conference
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• 1997.11a
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• pp.471-475
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• 1997

PWM-VSI based ac-drives have high nonlinearity due to dead-time in the inverter and the voltage drop across the switching devices. In this paper, we introduced a new nonlinear model of PWM-VSl including parastic capacitor and also showed validity of the model by circuit simulations and experiments. Furthermore, we proposed an on-line identification algorithm for the uncertain model parameters.

• Proceedings of the KIEE Conference
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• 1997.11a
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• pp.66-68
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• 1997

In this paper, our interest is the identification and control of nonlinear dynamic plant, induction motor, by using neural networks. We usually use vector control in the induction motor such as in the DC motor. When we go over the inputs of voltage source invertor, we can find that torque current and flux current couple each other in the induction motor. Before putting control inputs in the system, we should remove the coupling terms which we already know from them. But we should consider that cross coupling terms have time-varying variables. In this paper, we identified the parameter of induction motor by using neural networks and designed the controller with identified parameters. Through this procedure we obtained compensated inputs which are decoupled each other. Using induction motor currents control, we can make the d axis current hold constant value and control the q axis current at the same time.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.64 no.7
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• pp.1040-1046
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• 2015

This paper introduces GP(Genetic Programming) based color detection model for an object detection of humanoid robot vision. Existing color detection methods have used linear/nonlinear transformation of RGB color-model. However, most of cases have difficulties to classify colors satisfactory because of interference of among color channels and susceptibility for illumination variation. Especially, they are outstanding in degraded images from robot vision. To solve these problems, we propose illumination robust and non-parametric multi-colors detection model using evolution of GP. The proposed method is compared to the existing color-models for various environments in robot vision for real humanoid Nao.