• 제어로봇시스템학회:학술대회논문집
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• 제어로봇시스템학회 1996년도 한국자동제어학술회의논문집(국내학술편); 포항공과대학교, 포항; 24-26 Oct. 1996
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• pp.61-64
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• 1996

This paper presents a new approach to the design of self-tuning adaptive control system that is robust to the changing dynamic configuration as well as to the load variation factors using Digital signal processors for robot manipulators. TMS32OC50 is used in implementing real-time adaptive control algorithms to provide advanced performance for robot manipulator. In this paper, an adaptive control scheme is proposed in order to design the pole-placement self-tuning controller which can reject the offset due to any load disturbance without a detailed description of robot dynamics. Parameters of discrete-time difference model are estimated by the recursive least-square identification algorithm, and controller parameters are determined by the pole-placement method. Performance of self-tuning adaptive controller is illustrated by the simulation and experiment for a SCARA robot.

• Journal of Electrical Engineering and Technology
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• 제11권2호
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• pp.490-498
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• 2016

Proportional Integral Derivative (PID) controller tuning is an area of interest for researchers in many areas of science and engineering. This paper presents a new algorithm for PID controller tuning based on a combination of bacteria foraging and particle swarm optimization. BFO algorithm has recently emerged as a very powerful technique for real parameter optimization. To overcome delay in an optimization, combine the features of BFOA and PSO for tuning the PID controller. This new algorithm is proposed to combine both the algorithms to get better optimization values. The real time prototype model of paper machine is designed and controlled by using PIC microcontroller embedded with the programming in C language.

• 한국해양공학회지
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• 제3권2호
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• pp.551-551
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• 1989

In general, a remotely operated vehicle(ROV) operates at deep sea. The control system of ROV is composed of two local loops; the first loop placed on the surface vessel monitors and manipulates the attitude of the ROV using joystick, and the second part on the ROV automatically controls thrusters and acquires positional data. This paper presents a position control simulation of a ROV using an adaptive controller and discusses the control effects of two different conditions. The design of an adaptive control system is obtained by the application of a self-tuning controller with the minimization of an appropriate cost function. The parameters of the control system are estimated by a recursive least square method(RLS). In the simulation, a Runge-Kutta method is used for the numerical integration and the generated outputs are obtained by adding measurement errors. Additionally, this paper discusses the mathematical modelling of a ROV and make a survey of control systems.

• 한국해양공학회지
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• 제3권2호
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• pp.51-58
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• 1989

In general, a remotely operated vehicle(ROV) operates at deep sea. The control system of ROV is composed of two local loops; the first loop placed on the surface vessel monitors and manipulates the attitude of the ROV using joystick, and the second part on the ROV automatically controls thrusters and acquires positional data. This paper presents a position control simulation of a ROV using an adaptive controller and discusses the control effects of two different conditions. The design of an adaptive control system is obtained by the application of a self-tuning controller with the minimization of an appropriate cost function. The parameters of the control system are estimated by a recursive least square method(RLS). In the simulation, a Runge-Kutta method is used for the numerical integration and the generated outputs are obtained by adding measurement errors. Additionally, this paper discusses the mathematical modelling of a ROV and make a survey of control systems.

• 한국지능시스템학회논문지
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• 제6권3호
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• pp.40-50
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• 1996

By using the neural networks(NN) as system identifier, the on-line self tuning method for fuzzy controller(FC) is proposed. In theis method, the learning of NN is carried out during control operation of FC and the cinsequent parameters of FC is tuned on-line automatically by means of system output errors backpropagated through NN. The Sugeno fuzzy model with constants as consequent parameters is selected for simplifying computation. In procedures of parameter tuning, the gradient descent method is used and the gradient vectors for adjusting the weight of NN are transferred as controller output errors. To evaluate the performance, the proposed method is applied to the inverted pendulum system.

• Journal of Mechanical Science and Technology
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• 제19권9호
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• pp.1711-1719
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• 2005

In an electro statically actuated nanoelectromechanical system (NEMS) resonator, it is shown that both the resonance frequency and the resonance quality (Q) factor can be manipulated. How much the frequency and quality factor can be tuned by excitation voltage and resistance on a doubly-clamped beam resonator is addressed. A mathematical model for investigating the tuning effects is presented. All results are shown based on the feasible dimension of the nanoresonator and appropriate external driving voltage, yielding up to 20 MHz resonance frequency. Such parameter tuning could prove to be a very convenient scheme to actively control the response of NEMS for a variety of applications.

• 대한전기학회:학술대회논문집
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• 대한전기학회 2000년도 하계학술대회 논문집 D
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• pp.2584-2586
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• 2000

A drum level control is one of the most important control systems in thermal power plant. The control objective of drum level of boiler in thermal power plant is to maintain drum level at constant set-point regardless of disturbance such as main steam flow. The implemented drum level controller is the cascade PI controller. The important factor in drum level controller is the parameters of two PI controllers. The tuning of PI controller parameter is tedious and time-consuming job. In this paper, the relay feedback Ziegler - Nichols tuning method extended to auto-tune cascade PI drum level controller. Finally, the simulation result using boiler model in Power Plant shows the validity of auto-tuned cascade PI controller.

• 대한전기학회:학술대회논문집
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• 대한전기학회 2003년도 춘계학술대회 논문집 전기기기 및 에너지변환시스템부문
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• pp.248-252
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• 2003

This paper has a control method proposed for the effective self-tuning fuzzy speed control based on neural network of the induction motor indirect vector control. The vector control of an induction motor provides the decoupled control of the rotor flux magnitude and the torque producing current to performance is desirable. But, the drive performance often degrades for the machine parameter variations and its condition give rise to coupling of flux and torque current. The fuzzy speed control of an induction motor has the robustness about machine parameter variations compared with conventional PID speed control in a way. That proved to be some waf from the true. The purpose of this paper is to improve the adaptation by offering self-turning function to fuzzy speed controller. In this paper, the adaptive mechanism of fuzzy speed control in used ANN(Artificial Neural Network) technique is applied in an IFO induction machine drive, such that the machine can follow a reference model (an ideal field oriented machine) to achieve desired speed. In this paper proved the self-turning method of fuzzy controller has the robustness about parameter variation and the wide range of adaptation by simulation.

• Journal of Advanced Marine Engineering and Technology
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• 제31권3호
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• pp.293-300
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• 2007

The aim of this paper is to design a speed controller of a DC motor by selection of a PID parameters using genetic algorithm. The model of a DC motor is considered as a typical non-oscillatory, second-order system, And this paper compares three kinds of tuning methods of parameter for PID controller. One is the controller design by the genetic algorithm. second is the controller design by the model matching method third is the controller design by Ziegler and Nichols method. It was found that the proposed PID parameters adjustment by the genetic algorithm is better than the Ziegler & Nickels' method. And also found that the results of the method by the genetic algorithm is nearly same as the model matching method which is analytical method. The proposed method could be applied to the higher order system which is not easy to use the model matching method.

• 응용통계연구
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• 제29권4호
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• pp.643-656
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• 2016

고차원 자료(high dimensional data)는 변수의 수가 표본의 수보다 많은 자료로 다양한 분야에서 관측 또는 생성되고 있다. 일반적으로, 고차원 자료에 대한 회귀 모형에서는 모수의 추정과 과적합을 피하기 위하여 변수 선택이 이루어진다. 벌점화 회귀 모형(penalized regression model)은 변수 선택과 회귀 계수의 추정을 동시에 수행하는 장점으로 인하여 고차원 자료에 빈번하게 적용되고 있다. 하지만, 벌점화 회귀 모형에서도 여전히 조율 모수 선택(tuning parameter selection)을 통한 최적의 모형 선택이 요구된다. 본 논문에서는 벌점화 회귀 모형 중에서 대표적인 LASSO 회귀 모형을 기반으로 모형 선택의 기준들에 대한 LASSO 회귀 추정량의 편의가 어떠한 영향을 미치는지 모의실험을 통하여 수치적으로 연구하였고 편의의 보정의 필요성에 대하여 나타내었다. 실제 자료 분석에서의 영향을 나타내기 위하여, 폐암 환자의 유전자 발현량(gene expression) 자료를 기반으로 바이오마커 식별(biomarker identification) 문제에 적용하였다.