• Proceedings of the Korean Society of Precision Engineering Conference
• /
• 1993.10a
• /
• pp.533-538
• /
• 1993

본 논문에서는 퍼지제어 이론을 적용한 전지 유압 속도제어 시스템을 설계하였다, 최적의 퍼지 추론법을 유도하기 위해서 시뮬레이션 프로그램을 개발하여 최적의 샘플링 시간, A/D 및 D/A 변환기의 비트수를 결정하였고, 퍼지 입출력 변수의 형태, 퍼지 관계 행렬의 크기, 비퍼지화 방법 등을 시뮬레이션화하여 최적의 제어조건을 결정하였다, 전기유압 서어보 시스템에 적합한 퍼지 알고리즘은 Lsrsen 추론법, 비퍼지화 방법으로는 무게중심ㅂ버, 9*9 퍼지관계 행렬, 등간격의 삼각형 입출력 변수, 오차의 퍼지집합 및 오차 변화분의 퍼지집합이 각각 40과 5 일때 제어가 가장 잘 되었다. PID 제어방법과 비교할 때 퍼지제어가 우수한 성능을 보였으며,시스템의 등록성이 변할 때도 퍼지제어가 PID 제어 보다 적응이 잘 됨을 확인하였다.

• Journal of the Institute of Convergence Signal Processing
• /
• v.13 no.1
• /
• pp.56-62
• /
• 2012

This paper presents design and implementation of the speed controller for DC motor system using Embeded Target for TI C2000 DSP library in Matlab/Simulink is introduced. Speed controller are easily design and implemented by using the Matlab/Simulink program. Feedback of motor speed is processed through eZdsp F2812 AID converter using encoder and pulse meter as speed sensor. Real-time program of controller is drawn using Simulink and converted program code for speed control of P control, PID control and parameter estimation base adaptive control is downloaded into the TI eZdsp 2812 board. Experiments were carried out to examine validity of speed response for implemented controllers. And even if controlled plant becomes alteration studied controller design and implementation easily method.

• Journal of the Institute of Convergence Signal Processing
• /
• v.16 no.3
• /
• pp.108-114
• /
• 2015

Ballast water is carried by ships to ensure stability, trim and structural integrity. When a ship loads cargo, the ballast water is discharged. When foreign marine microorganisms are introduced into new marine environments, they pose a threat to the local marine ecological system. UV system is commonly used for the disinfection of waste and surface water. This method would not be as efficient because some species do survive to form viable populations, much of the sediment and organisms at the bottom of tanks, and may become serious pests. In this paper, we designed and implemented ballast water treatment system using fuzzy PID controller to prevent lamp damage, and to reduce the formation of the viable populations. The experiments were conducted with ballast water treatment system using fuzzy PID controller with short time exposure to the temperature above $40^{\circ}C$. This system was shown to be effective by significantly reducing bacterial population and lamp life extension through appropriate temperature of ballast water.

• Transactions of the Korean Society of Mechanical Engineers A
• /
• v.27 no.9
• /
• pp.1463-1468
• /
• 2003

This paper presented the real-time self-tuning learning control based on evolutionary computation, which proves its superiority in finding of the optimal solution at the off-line learning method. The individuals of the populations are reduced in order to learn the evolutionary strategy in real-time, and new method that guarantee the convergence of evolutionary mutations is proposed. It is possible to control the control object slightly varied as time changes. As the state value of the control object is generated, evolutionary strategy is applied each sampling time because the learning process of an estimation, selection, mutation is done in real-time. These algorithms can be applied; the people who do not have knowledge about the technical tuning of dynamic systems could design the controller or problems in which the characteristics of the system dynamics are slightly varied as time changes.

• Proceedings of the Korean Institute of Intelligent Systems Conference
• /
• 2002.12a
• /
• pp.84-87
• /
• 2002

This paper presents an adaptive PID control scheme with fuzzy model for nonlinear system. TSK(Takagi-Sugeno-Kang) fuzzy model was used to estimate the error of control input, and the parameter of PID controller was adapted from the error The parameter of TSK fuzzy model was also adapted to plant by comparing the activity output of plant and model output. PID controller which was adapted the uncertainty of nonlinear plant and the change of parameter can be designed by using the presented method. The usefullness of algorithm which was proposed by the simulation of several nonlinear system was also certificated.

• Journal of the Korean Institute of Intelligent Systems
• /
• v.8 no.2
• /
• pp.106-116
• /
• 1998

A position control algorithm of a flexible manipulator is studied. The proposed algorithm is based on an ACFAC(Automatic Constructed Fuzzy Adaptive Controller) system based on the neural network learning algorithms. The proposed system learns membership functions for input variables using unsupervised competitive learning algorithm and output information using supervised outstar learning algorithm. ACFAC does not need a dynamic modeling of the flexible manipulator. An ACFAC is designed that the end point of the flexible manipulator tracks the desired trajectory. The control input to the process is determined by error, velocity and variation of error. Simulation and experiment results show a robustness of ACFAC compared with the PID control and neural network algorithms.

• The Transactions of the Korean Institute of Electrical Engineers D
• /
• v.49 no.4
• /
• pp.201-212
• /
• 2000

In this paper, an adaptive mechanism based on immune algorithm is designed and it is applied for the autonomous guided vehicle(AGV) driving. When the immune algorithm is applied to the PID controller, there exists the cast that the plant is damaged due to the abrupt change of PID parameters since the parameters are adjusted almost randomly. To solve this problem, a neural network is used to model the plant and the parameter tuning of the model is performed by the immune algorithm. After the PID parameters are determined in this off-line manner, these gains are then applied to the plant for the on-line control using immune adaptive algorithm. Moreover, even though the neural network model may not be accurate enough intially, the weighting parameters are adjusted to be accurate through the on-line fine tuning. The computer simulation for the control of steering and speed of AGV is performed. The results show that the proposed controller has better performances than other conventional controllers.

• The Proceedings of the Korean Institute of Illuminating and Electrical Installation Engineers
• /
• v.8 no.3
• /
• pp.64-70
• /
• 1994

We propose a new method to deal with optimal auto-tuning of the PID controller which is used to process control in various fields. First of all, in this method, 1st order system which was modeled from the unit step response of the system is Pade-approximated, then initial values are determined by the Ziegler-Nichols method. Finally, we can find the parameters of PID controller so as to maximize the fuzzy criterion function which includes the maximum overshoot, damping ratio, rising time and settling time. The Proposed method also shows good adaptability for variations in characteristics and dead m e of the system.

• Proceedings of the KIEE Conference
• /
• 1997.07b
• /
• pp.702-704
• /
• 1997

In this paper, we applied self-tuning controller with I-PD type to process with time delay's. Process parameters are estimated by the recursive least squares algorithm, and optimal gains are obtained. This paper shows self-tuning controller with I-PD type performs better than that of general PID type for the nonlinear system with sudden change of set-points.

• Journal of Institute of Control, Robotics and Systems
• /
• v.4 no.2
• /
• pp.209-214
• /
• 1998

In this paper, we are dealing with the problem of controlling unknown nonlinear dynamical system by using neural networks. A novel error self-recurrent(ESR) neural model is presented to perform black-box identification. Through the various outcome of the experiment, a new neural network is seen to be considerably faster than the BP algorithm and has advantages of being less affected by poor initial weights and learning rate. These characteristics make it flexible to design the controller in real-time based on neural networks model. In addition, we design an adaptive PID controller that Keyser suggested by using ESR neural networks, and present a method on the implementation of adaptive controller based on neural network for practical applications. We obtained good results in the case of robot manipulator experiment.