• KIEE International Transaction on Systems and Control
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• v.2D no.2
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• pp.78-91
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• 2002

In the thermal power plant, there are six manipulated variables: main steam flow, feedwater flow, fuel flow, air flow, spray flow, and gas recirculation flow. There are five controlled variables: generator output, main steam pressure, main steam temperature, exhaust gas density, and reheater steam temperature. Therefore, the thermal power plant control system is a multinput and output system. In the control system, the main steam temperature is typically regulated by the fuel flow rate and the spray flow rate, and the reheater steam temperature is regulated by the gas recirculation flow rate. However, strict control of the steam temperature must be maintained to avoid thermal stress. Maintaining the steam temperature can be difficult due to heating value variation to the fuel source, time delay changes in the main steam temperature versus changes in fuel flow rate, difficulty of control of the main steam temperature control and the reheater steam temperature control system owing to the dynamic response characteristics of changes in steam temperature and the reheater steam temperature, and the fluctuation of inner fluid water and steam flow rates during the load-following operation. Up to the present time, the Proportional-Integral-Derivative Controller has been used to operate this system. However, it is very difficult to achieve an optimal PID gain with no experience, since the gain of the PID controller has to be manually tuned by trial and error. This paper focuses on the characteristic comparison of the PID controller and the modified 2-DOF PID Controller (Two-Degrees-Freedom Proportional-Integral-Derivative) on the DCS (Distributed Control System). The method is to design an optimal controller that can be operated on the thermal generating plant in Seoul, Korea. The modified 2-DOF PID controller is designed to enable parameters to fit into the thermal plant during disturbances. To attain an optimal control method, transfer function and operating data from start-up, running, and stop procedures of the thermal plant have been acquired. Through this research, the stable range of a 2-DOF parameter for only this system could be found for the start-up procedure and this parameter could be used for the tuning problem. Also, this paper addressed whether an intelligent tuning method based on immune network algorithms can be used effectively in tuning these controllers.

• 제어로봇시스템학회:학술대회논문집
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• 1995.10a
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• pp.135-137
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• 1995

A new design method is proposed for a fuzzy PD controller. By analyzing phase plane characteristics we can build and optimize the rule base of fuzzy logic controller. Also, a new gain tuning method is used to improve performance in the transient and steady state. The improved performance of the new methodology is shown by an application to the design of control system with a highly nonlinear actuator.

• The Transactions of the Korean Institute of Power Electronics
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• v.19 no.3
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• pp.290-296
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• 2014

This paper presents a design strategy for the control of the Z-source inverter (ZSI). For the Z-network capacitor voltage control, the average current model is derived to describe the dynamics of the voltage control and the controller outputs the average current command for the capacitor. Z-network inductor current reference is derived from the average current model of the Z-network capacitor. The inner current control loop outputs the average voltage command for the Z-network inductor and the shoot-through duty ratio of the ZSI is calculated from the output using the average voltage model of the Z-network inductor. The gain values of the current and voltage controllers are directly obtained by the Z-network parameters and desired bandwidth of each controller without a gain tuning process.

• Journal of Institute of Control, Robotics and Systems
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• v.17 no.7
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• pp.634-640
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• 2011

When a servo position controller of a robot or a driving units is composed of a PID controller, servomechanism which is modelled is composed of current, velocity and position control loops. After this model is simulated, the technique operating gain of each controller is suggested. The model consists of current, velocity and position controllers from the inside to the outside gradually. Also, to combine velocity and position controllers with 2 order system, simulation is performed after current controllers are composed, which are able for current loop to work ideally. If a current controller is treated with constant, it is possible for velocity and position controller to consist of controller into 2 order system. The technique is verified by applying T-company servo motor which is much more applied to current, velocity and position controller robots.

• Proceedings of the KIEE Conference
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• 2001.10a
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• pp.165-167
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• 2001

This paper presents a study on successive approximation measurement of mechanical parameters for motor control system. At the first step of servo system installation, control system gain tuning is troublesome work. Recently, auto-tuning method of motion controller for motor drive system is based on parameter measurement and identification. On the case of first order mechanical system (mechanical parameters are modified by simple inertia and friction), it is necessary for good response to get the accurate measurement or identification of the mechanical parameters. In this paper, novel method applies the binary successive approximation measurement to the inertia and friction coefficient. Computer simulation and experiment for the proposed method will show verification of accuracy and usefulness.

• Proceedings of the Korean Society of Marine Engineers Conference
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• 2001.11a
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• pp.112-119
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• 2001

Parameter tuning methods by Ziegler-Nickels for control systems are generally classified into Z-N(1) and Z-N(2). The purpose of this paper is to describe what relations exist between methods of Z-N(1) and Z-N(2), or how Z-N(1) method can be originated from Z-N(2) method by analyzing one loop control system of P or PI controller and time delay process. The formulas of Z-N(1) consist of process parameters, L(time delay), $K_m$(gain) and $T_m$(time constant), but Z-N(2) method is based only on the ultimate gain $K_u$ and the ultimate period $T_u$ acquired normally by practical trial without any parameters of Z-N(1). In this paper, for the first step to seek mutual relations, the simple formulas of Z-N(2) are transformed into the formulas composed of the same parameters as Z-N(1) which is derived from the analysis of frequency characteristics. Then, the approximation of the actual ultimate frequency is proposed as important premise in the translation between Z-N(1) and (2). Such equalization and approximation brings a simple approximated formula which can explain how Z-N(1) is originated from the Z-N(2) in the form of formula. And a model system is adopted to compare the approximated formula to Z-N(1) and Z-N(2) methods, the results of which show the effectiveness of the proposals.

• Journal of IKEEE
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• v.28 no.2
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• pp.136-144
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• 2024

In this paper, we propose a PID controller for altitude control of quadrotor system with experimental analysis. The Routh-Hurwitz test is applied to analyze the system to which our proposed controller is applied. We also summarize experimental data in which the gain values of kP, kI, and kD are changed using MATLAB and Simulink based on mathematical modeling of the quadrotor system. Based on the summarized experimental data, we analyze the effect of changes in each gain values (k_P, k_I, k_D) of PID controller on altitude control of quadrotor, and present an algorithm for tuning the PID controller gain values. The PID controller with the proposed algorithm is applied to AR.Drone system, subsequently and result are verifised through experiments.

• Journal of Institute of Control, Robotics and Systems
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• v.19 no.9
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• pp.839-845
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• 2013

A CSTR (Continuous Stirred Tank Reactor) is a highly nonlinear process with varying parameters during operation. Therefore, tuning of the controller and determining the transition policy of controller parameters are required to guarantee the best performance of the CSTR for overall operating regions. In this paper, a methodology employing the 2DOF (Two-Degree-of-Freedom) PID controller, the anti-windup technique and a fuzzy gain scheduler is presented for the temperature control of the CSTR. First, both a local model and an EA (Evolutionary Algorithm) are used to tune the optimal controller parameters at each operating region by minimizing the IAE (Integral of Absolute Error). Then, a set of controller parameters are expressed as functions of the gain scheduling variable. Those functions are implemented using a set of "if-then" fuzzy rules, which is of Sugeno's form. Simulation works for reference tracking, disturbance rejecting and noise rejecting performances show the feasibility of using the proposed method.

• Journal of the Korean Institute of Telematics and Electronics B
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• v.29B no.8
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• pp.15-23
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• 1992

Neural network control has many innovative potentials for fast, accurate and intelligent adaptive control. In this paper, two kinds of neurocontrol architectures for the dynamic control of robot manipulators are developed. One is based on a System Identification and Control scheme and the other is based on the Feedback-Error leaming scheme. Both of the proposed architectures use an inverse dynamic neurocontroller in parallel with a linear neurocontroller. The difference is that the first architecture uses the system identifier to get the signals used for training neurocontrollers, while the second architecture uses a properly defined energy function. Compared with the previous types of neurocontrollers which are using an inverse dynamic neurocontroller and a fixed PD gain controller, the proposed architectures not only eliminate the painful process of the fixed gain tuning but also exhibit superior peformances because the linear neurocontroller can adapt its gains according to the applied task. This superior performance is tested and verified through computer simulation of the dynamic control of the PUMA 560 arm.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 2003.09a
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• pp.525-530
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• 2003

This paper focuses on design of nonlinear power plant controller using immune based multiobjective fuzzy approach. The thermal power plant is typically regulated by the fuel flow rate, the spray flow rate, and the gas recirculation flow rate. However, Strictly maintaining the steam temperature can be difficult due to heating value variation to the fuel source, time delay changes in the main steam temperature. the change of the dynamic characteristics in the steam-turbine system. Up to the present time, PID Controller has been used to operate this system. However, it is very difficult to achieve an optimal PID gain with no experience, since the gain of the PID controller has to be manually tuned by trial and error. These parameters tuned by multiobjective based on immune network algorithms could be used for the tuning of nonlinear power plant.