• Journal of Bio-Environment Control
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• v.8 no.3
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• pp.152-163
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• 1999

It is required to analyze the controlled response of air temperature in greenhouse according to control techniques for precise control. In this study, a mathematical model was established for air heating of greenhouse with hot-water heating system The parameters of the model were decided by regression analysis using reference data measured at the greenhouse being heated In the simulation for the digital control of air temperature in the greenhouse, the mathematical model to evaluate the control performances was used. Tested control methods were ON-OFF contpol, p control, rl control and PID control. The mathematical model represented by inside air temperature ( T$_{i}$), hot-water temperature (T$_{w}$) in heating pipe and outside air temperature (T$_{o}$) was expressed as a following discrete time equation ; T$_{i}$($textsc{k}$＋1)＝ 0.851.T$_{i}$($textsc{k}$)＋0.055.T$_{w}$($textsc{k}$)＋0.094.T$_{o}$($textsc{k}$) Control simulations for various control methods showed the settling time, the overshoot and the steady state nor as follows; infinite time, 3.5$0^{\circ}C$, 3.5$0^{\circ}C$ for ON-OFF control : 30min 2.37$^{\circ}C$, 0.51$^{\circ}C$ for P control; 21min, 0.0$0^{\circ}C$, 0.23$^{\circ}C$ for PI control; 18min 0.0$0^{\circ}C$, 0.23$^{\circ}C$ for PID control, respectively. PI and PID controls appeared to be optimal control methods. There was no effect of differential gain on the heating process but much effect of integral gain on it.on it.

• Journal of the Korean Institute of Intelligent Systems
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• v.8 no.5
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• pp.29-38
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• 1998

This paper proposes the neural network self-tuning PID controller for the load frequency control of 2- areas power system, namely, the prompt convergence of frequency and tie-line power flow deviation. The neural network applied to computer simulation consists of neurons of two inputs, ten hiddens and tliree outputs layer. Neurons of two inputs layer receive the error and its change rate of the system and cutputs layer consists of three neurons for the parameters of the PID controller. The simulation results shows that the proposed neural network self-tuning PID controller is superior to conventional control t~:chniques(Optimal, PID) in dynamic response and control performance.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.20 no.11
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• pp.2107-2112
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• 2016

This paper is designed to develop a Gimbal control stabilizer for drones Gimbal system control for drone for 3D image to make sure clean image in the shaking and wavering environments of drone system. The stabilizer is made of tools which support camera modules and IMU(Inertial Measurement Unit) sensor modules follow exact angles, which can brock vibrations outside of the camera modules. It is difficult for the camera modules to get clean image, because of irregular movements and various vibrations produced by flying drones. Moreover, a general PID controller used for the movements of rolling, pitching and yawing in order to control the various vibrations of various frequencies needs often to readjust PID control parameters. Therefore, this paper aims to conduct the Intelligent-PID controller as well as design the Gimbal control stabilizer to get clean images and to improve irregular movements and various vibrations problems referenced above.

• 제어로봇시스템학회:학술대회논문집
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• 2000.10a
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• pp.127-127
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• 2000

The anti-windup schemes developed so far are summarized and the similarities/differences schemes are discussed. The anti-windup schemes are applied to a DC servomotor control system with PID controller to perform comparative study and sensitivity analysis. Based on those results, some criteria for choosing anti-windup scheme are suggested. The results of this study provide a very useful guideline for selecting and designing the anti-windup scheme for various types of PID control systems.

• Journal of the Korean Society for Precision Engineering
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• v.13 no.5
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• pp.139-147
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• 1996

PID control has been widely used for real control systems. Particularly, there are many researches on control schemes of tuning PID gains. However, to the best of our knowledge, there is no result for discrete-time systems with unknown time-delay and unknown system parameters. On the other hand, Generalized predictive control has been reported as a useful self-tuning control technique for systems with unknown time-delay. So, in this study, based on minimization of a GPC criterion, we present a self-tuning PID control algorithm for unknown papameters and unknown time-delay system. A numerical simulation was presented to illustrate the effectiveness of this method.

• The Transactions of the Korean Institute of Power Electronics
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• v.6 no.1
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• pp.49-56
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• 2001

This paper presents a new approach to the problem based on neural network methods. Instead of using general controllers, neural networks PID control are used to control AC servo motor. The most popular and widely used control method in servo system control loops is PID type. PID controller has the features of simple structure, stability and reliability. But it has limitations in complex system control and can not remain above virtues under the conditions of parameters uncertain and environment uncertainties. AC servo motor controller is designed for drive of the cartesian coordinate type for machine tools.

• Journal of the Institute of Convergence Signal Processing
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• v.10 no.3
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• pp.186-192
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• 2009

PID controllers are simple in structure and easy for implementation. However, they may produce large overshoots and over-oscillatory responses. Combining PID control with other control techniques often results in advanced hybrid schemes that are able to improve pure PID controllers. This paper proposes hybrid controller for position control system of servo motor. The proposed controller is composed of a subcontroller and a parallel PID controller. The subcontroller improves the transient system performance while the PID controller is mainly responsible for the steady-state system performance. A very promising advantage of this hybrid scheme, in terms of controller synthesis, is that the subcontrollers and controller components can be designed separately. Systematic design methods for various controller components are developed. The proposed hybrid scheme is applied to a DC motor position servo system. The effectiveness of the proposed controller is verified through the computer simulation results.

• Journal of the Korean Institute of Intelligent Systems
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• v.22 no.2
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• pp.239-245
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• 2012

This paper proposes a novel optimum design method for the PID controller of magnetic levitation-based Rail-Guided Vehicle(RGV) by a genetic algorithm using clone selection method and a new performance index function with performances of both time and frequency domain. Generally, since an attraction type levitation system is intrinsically unstable and requires a delicate controller that is designed considering overshoot and settling time, it is difficult to completely satisfy the desired performance through the methods designed by conventional performance indexes. In the paper, the conventional performance indexes are analyzed and then a new performance index for Maglev-based RGV is proposed. Also, an advanced genetic algorithm which is designed using clonal selection algorithm for performance improvement is proposed. To verify the proposed algorithm and the performance index, we compare the proposed method with a simple genetic algorithm and particle swarm optimization. The simulation results show that the proposed method is more effective than conventional optimization methods.

• Journal of the Korean Society for Aeronautical & Space Sciences
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• v.39 no.7
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• pp.618-626
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• 2011

In order to complete missions in a complicated terrain or highly dangerous area, an unmanned aerial vehicle(UAV) needs a fine controller to precisely follow the desired path. A PID controller used for the path following feeds forward path curvature information to the control input to improve the path following performance. High gain for PID controller is necessary to follow path tightly. However the high gain could cause instability or performance degradation when the vehicle has slow dynamics. We present PID controller design method which considers response delay of vehicle as well as path curvature. In order to obtain path curvature the desired path is described as a 3rd order polynomial by applying cubic spline interpolation. We apply the proposed controller to the path following of a UAV which is operated in high altitude and has very slow lateral dynamics. The lateral dynamics are modelled as a first order delayed system in the controller design. Nonlinear simulation shows the UAV with proposed controller follows an arbitrary path very tightly.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.19 no.11
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• pp.2534-2539
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• 2015

Method for regulating voltage using a generator voltage regulating device (AVR) is divided in an existing analog system and a digital replacement. Typically, to adjust the voltage by using a brushless excitation system of the type to be reduced for a voltage change under all. The control method of the AVR as a PID (proportional-integral-differential) control method is widely used. However, the control scheme is to reduce the transient response of the control parameters of the controller to the control object. Therefore, if the control target should change, there is a problem, reset the parameters of the controller again. In this study, without having to reset the parameters of the controller for the parameter variations to be controlled iPID (intelligent PID) using a controller designed to obtain a generator AVR system voltage variation is small in response to full load is applied to and through simulations and experiments improved transient response.