• Journal of Sensor Science and Technology
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• v.24 no.6
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• pp.412-418
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• 2015

In this paper, a new method for obtaining PID coefficients which are essential to a temperature stabilization process has been proposed. This method starts from measuring the open loop transfer function of the module, then the closed loop transfer function embodying PID control can be produced based on this. Finally, the simulations using a few PID coefficients and the performance analysis for those results provide the best PID coefficients which are effective in a fast setting to a target temperature, a less current needed, and less deviation from steady state. The measurement using the derived PID coefficients, $K_p=1.6$,$K_i=0.8$,$K_d=0.3$ showed $T_s=7.4[sec]$, %OS = 16, and stabilization within ${\pm}0.02[^{\circ}C]$ for several hours. In addition to light sources like SOA, the proposed method can be utilized for any device needs temperature stabilization.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.15 no.3
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• pp.536-544
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• 2011

In this paper, a controller for two wheeled inverted pendulum type robot is designed to have more stable balancing capability than conventional controllers. Traditional PID control structure is chosen for the two wheeled inverted pendulum type robot, and proper gains for the controller are obtained for specified user's weights using trial-and-error methods. Next a neural network is employed to generate PID controller gains for more stable control performance when the user's weight is arbitrarily selected. Through simulation studies we find that the designed controller using the neural network is superior to the conventional PID controller.

• Journal of Institute of Control, Robotics and Systems
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• v.13 no.1
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• pp.19-25
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• 2007

The MFC(Mass Flow Controller) is an equipment that measures and controls mass flow rates of fluid. Most of the HFC system is still using the PID algorithm. The PID algorithm shows superior performance on the MFC system. But the PID algorithm in the MFC system has a few problems as followed. The characteristic of the MFC system is changed according to the operating environment. And, when the piezo valve that uses the control valve is assembled in the MFC system, a coupling error is generated. Therefore, it is very difficult to find out the exact parameters of MFC system. In this paper, we propose adaptive PID algorithm in order to compensate these problems of a traditional PID algorithm. The adaptive PID algorithm estimates the parameters of MFC system using LMS(Least Mean Square) algorithm and calculates the coefficients of PID controller. Besides, adaptive PID algorithm shows better transient response because adaptive PID algorithm includes a feedforward. And we implement MFC system using proposed adaptive PID algorithm with self-tuning and Ziegler and Nickels's method. Finally, comparative analysis of the proposed adaptive PID and the traditional PID is shown.

• Proceedings of the KIEE Conference
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• 2007.04a
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• pp.119-121
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• 2007

PID control is windely used to control stable processes, however, its application to integrating processes is less common. In this paper, we proposed a simple PID controller tuning method for integrating processes with time delay to meet a sensitive function $M_s$. With the proposed PID tuning method, we can obtain stable integrating processes using PD controller in inner feedback loop and a loop transfer function with desired stable specification. This guarantees both robustness and performance. Simulation examples are given to show the good performance of the proposed tuning method to other methods.

• Proceedings of the KIEE Conference
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• 1998.11b
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• pp.499-501
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• 1998

In this paper, we present a self-compensating PID controller which consists of a conventional PID controller that controls the linear components and a neural controller that controls the higher order and nonlinear components. This controller is based on the Harris's concept where he explained that the adaptive controller consists of the PID control term and the disturbance compensating term. The resulting controller's architecture is also found to be very similar to that of Wang's controller. This controller adds a self-tuning ability to the existing PID controller without replacing it by compensating the control errors through the neuro-controller. When applied to an actual magnetic levitation system which is known to be very nonlinear, it has also produced an excellent results.

• Proceedings of the KIEE Conference
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• 2001.07d
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• pp.2108-2110
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• 2001

Used analog control systems have been converted into digital control systems due to performance degradation and difficulty of maintenance. There are few domestic DCS (Distribued Control System)s that have been applied to power plant. To apply a domestic DCS to power plant, the reliability, redundacny, and fault tolerance of DCS is important. Besides those items, the control action of control function block is also important. In this paper, we describe the requirements that PID control function block has to have, and implement a PID control function block that satisfies those requirements. Finally, real control result using an implemented PID function block in thermal power plant shows the validity of an implemented PID control function block.

• Journal of the Institute of Convergence Signal Processing
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• v.5 no.2
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• pp.138-142
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• 2004

This paper proposes the design of the model following control system using the PID control structure. PID control system became model following control by inserting new pre-compensator in order to improve control performance in discrete-time region. Gain of the PID controller needs to be readjusted when response of system changes due to disturbance or load fluctuation. Performance of control system improves by joining neural network to PID control system because performance of control system depends largely on each PID gain in PID control system. And the games of the PID controller in the proposed control system are automatically adjusted by back-propagation algorithm of the neural network. Angular position of DC servo motor is selected as a plant in order to verify control performance in model following control. After it is applied to the position control system, it's performance is verified through computer experiment.

• 제어로봇시스템학회:학술대회논문집
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• 2004.08a
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• pp.758-761
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• 2004

This paper proposes a design and implementation of multiplierless digital PID (Proportional-Integral-Derivative) controller using FPGA (Field Programmable Gate Array) for controlling the speed of DC motor in digital system. The multiplierless PID structure is based on Distributed Arithmetic (DA). The DA is an efficient way to compute an inner product using partial products, each can be obtained by using look-up table. The PID controller is designed using MATLAB program to generate a set of coefficients associated with a desired controller characteristics. The controller coefficients are then included in VHDL (Very high speed integrated circuit Hardware Description Language) that implements the PID controller onto FPGA. MATLAB program is used to activate the PID controller, calculate and plot the time response of the control system. In addition, the hardware implementation uses VHDL and synthesis using FLEX10K Altera FPGA as target technology and use MAX+plusII program for overall development. Results in design are shown the speed performance and used area of FPGA. Finally, the experimental results can be shown when compared with the simulation results from MATLAB.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2009.05a
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• pp.185-188
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• 2009

In this paper, Position Controller for balance of Seesaw System design using PID Algorithm. Seesaw System is that it's system use widely to analyze of ship or flight dynamics, Inverted Pendulumand, Robot System, manage system for theory of modern control system and all sorts of analysis. In case of Seesaw System, it's necessity that understand and analysis of system and correct selection of parameter because the system is strong nonlinear control system. It guarantees efficiency and stability to adapt quickly for disturbance or change of controller from PID Algorithm of guarantee safe from simple and long history and PSO(Particle Swarm Optimization) that sort of metaheuristic optimization that need to accuracy and fast PID parameter tuning.

• 제어로봇시스템학회:학술대회논문집
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• 2000.10a
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• pp.86-86
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• 2000

This paper describes a neural network based fuzzy type PID control scheme. The PID controller is being widely used in industrial applications. however, it is difficult to determine the appropriate PID gains for (he nonlinear system control. In this paper, we re-analyzed the fuzzy controller as conventional PID controller structure, and proposed a neural network based fuzzy type PID controller whose scaling factors were adjusted automatically. The value of initial scaling factors of the proposed controller were determined on the basis of the conventional PID controller parameters tuning methods and then they were adjusted by using neural network control techniques. Proposed controller was simple in structure and computational burden was small so that on-line adaptation was easy to apply to. The result of practical experiment on the magnetic levitation system, which is known to be hard nonlinear, showed the proposed controller's excellent performance.