• Journal of Institute of Control, Robotics and Systems
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• v.13 no.4
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• pp.279-285
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• 2007

The objective of the research is two fold. The first is to design and propose a stable and robust learning control algorithm. The controller is CMAC Learning Controller which consists of a model-based controller, such as LQR or PID, as a reference control and a CMAC. The second objective is to implement a reference control and CMAC at two different sampling rates. Generally, a conventional controller is designed based on a mathematical plant model. However, increasing complexity of the plant and accuracy requirement on mathematical models nearly prohibits the application of the conventional controller design approach. To avoid inherent complexity and unavoidable uncertainty in modeling, biology mimetic methods have been developed. One of such attempts is Cerebellar Model Articulation Computer(CMAC) developed by Albus. CMAC has two main disadvantages. The first disadvantage of CMAC is increasing memory requirement with increasing number of input variables and with increasing accuracy demand. The memory needs can be solved with cheap memories due to recent development of new memory technology. The second disadvantage is a demand for processing powers which could be an obstacle especially when CMAC should be implemented in real-time. To overcome the disadvantages of CMAC, we propose CMAC learning controller with multiple sampling rates. With this approach a conventional controller which is a reference to CMAC at high enough sampling rate but CMAC runs at the processor's unoccupied time. To show efficiency of the proposed method, an inverted pendulum controller is designed and implemented. We also demonstrate it's possibility as an industrial control solution and robustness against a modeling uncertainty.

• International Journal of Control, Automation, and Systems
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• v.5 no.4
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• pp.436-443
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• 2007

In this paper, an intelligent method called BELBIC (Brain Emotional Learning Based Intelligent Controller) is used to control of Locally Linear Neuro-Fuzzy Model (LOLIMOT) of Washing Machine. The Locally Linear Neuro-Fuzzy Model of Washing Machine is obtained based on previously extracted data. One of the important issues in using BELBIC is its parameters setting. On the other hand, the controller design for Washing Machine is a multi objective problem. Indeed, the two objectives, energy consumption and effectiveness of washing process, are main issues in this problem, and these two objectives are in contrast. Due to these challenges, a Multi Objective Genetic Algorithm is used for tuning the BELBIC parameters. The algorithm provides a set of non-dominated set points rather than a single point, so the designer has the advantage of selecting the desired set point. With considering the proper parameters after using additional assumptions, the simulation results show that this controller with optimal parameters has very good performance and considerable saving in energy consumption.

• The Transactions of the Korea Information Processing Society
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• v.5 no.11
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• pp.2908-2914
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• 1998

This paper presents an intelligent signal for controling the traffic lights on traffic junction network with dynamic traffic flow, When a junction is connected to adjacent junctions on four sides. Prior researches have been done on the single traffic junction. However, it is dificult to apply single junction controller to real traffic situation. In this paper, we develop a fuzzy taffic network controller which adjusts the extension time of current green phase by using teh fuzzy input variables such as the number of entering cars at the green light, the number of waiting cars during the red light, and the traffic volume. The proposed method was compared to the existing junction signal control methods on controllers in terms of average delay time of cars and the cost function defined in this paper.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.10 no.1
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• pp.197-205
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• 2006

This paper presents the moving object tracking method using vision system. In order to track object in real time, the image of moving object have to be located the origin of the image coordinate axes. Accordingly, Fuzzy Control System is investigated for tracking the moving object, which control the camera module with Pan/Tilt mechanism. Hereafter, so the this system is applied to mobile robot, we design and implement image processing board for vision system. Also fuzzy controller is implemented to the StrongArm board. Finally, the proposed fuzzy controller is useful for the real-time moving object tracking system by experiment.

• Journal of IKEEE
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• v.3 no.1 s.4
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• pp.109-117
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• 1999

This paper describes the quick and precise control of the wafer temperature essential in rapid thermal processing(RTP). The bang-bang plus PID controller structure is introduced to satisfy rapid ramp-up rate and reduce overshoot and steady state error. The controller employs the PID action when the magnitude of the error between reference signal and the output temperature signal is smaller than some prescribed value. To find PID gains, the plant(autoregressive) model is first identified and Kappa-Tau tuning rule is used. The developed controller is applied to experimental RTP apparatus, and performances are evaluated.

• Proceedings of the Korea Institute of Convergence Signal Processing
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• 2001.06a
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• pp.101-104
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• 2001

In this paper, we propose the Digital Automatic Gain Controller for IEEE 802.11a High-speed Physical Layer in the 5 GHz Band. The input gain is estimated by calculating the energy of the training symbol that is a synchronizing signal. The renewal gain is calculated by comparing the estimated gain with the ideal gain. The renewal gain is converted into the controlled voltage for GCA to reduce or amplify the input signals. We used a piecewise-linear approximation to reduce the hardware size. The gain control is performed seven times to provide more accurate gain control. The proposed automatic gain controller is designed with VHDL and verified by using the Xilinx FPGA.

• Journal of the Institute of Convergence Signal Processing
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• v.3 no.2
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• pp.76-82
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• 2002

The objective of this paper is to design a robust controller for a flexible robot arm manipulator using LMI(Linear Matrix Inequality) theory, and confirm its effectiveness through experimentation. We first describe a modeling Process of the flexible arm in order to get a mathematical model, and then discuss how to approximately obtain the uncertainty of the model for robust control. As to the control system design, we adopt the LMI-based H$_{\infty}$ synthesis algorithm which has the merits of eliminating the regularity restrictions attached to the Riccati-based methods. As a result of this, we can cope with the parameter variation (that is, modeling uncertainty) due to the tip-load variation. Finally we confirm the effectiveness of the controller through experiment and simulation.

• Journal of the Institute of Convergence Signal Processing
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• v.5 no.1
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• pp.40-45
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• 2004

This paper presents the parallel distributed control scheme for shaping of the bent glass. The designed system consists of a PC, a main controller and 11 servo-controllers, the precision motion controllers. Each elements are connected by using RS-232C and 8-bit data bus. In order to guarantee the stability and the control performance, we use a precision PID motion controller and a H-bridge on the servo-drivers. PC calculates position values of 11 DC motors by using the pre-determined curvature value and offers the user interface environment operator. The main controller provides the control instructions and parameter values to 11 servo-controllers by chip enable signal, simultaneously. Using the received commands and parameter values, the servo-controllers control the positions of the DC motors based on PID control scheme.

• Journal of the Institute of Convergence Signal Processing
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• v.5 no.1
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• pp.60-65
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• 2004

Induction heating system is time varying system around curie point. So, it has many troubles which are system shut down and change the load impedance. In this paper has been designed the parallel resonant inverter which controlling the constant power and tracking the load resonant frequency with PLL is possible, in order to minimize switching losses and solve it's many troubles. The current full-bridge type parallel resonant inverter of an induction heating system was composed of IGBT in switching device. For regulating the output power of an induction heating system, the Fuzzy logic controller is used. The Fuzzy controller makes the control signal for a stable power regulating control and when reference is changed, it is superior to adaptability. It has been evaluated a stable behavior for a noise with switching and a load disturbance.

• Journal of the Institute of Convergence Signal Processing
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• v.12 no.1
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• pp.62-66
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• 2011

This paper proposes a parallel architecture of random signal-based learning (PRSL), merged with simulated annealing (SA), to optimize the fuzzy logic controller (FLC). Random signal-based learning (RSL) finds the local optima very well, whereas it can not finds the global optimum in a very complex search space because of its serial nature. To overcome these difficulties, PRSL, which consists of serial RSL as a population, is considered. Moreover, SA is added to RSL to help the exploration. The validity of the proposed algorithm is conformed by applying it to the optimization of a FLC for the inverted pendulum.