• Journal of Institute of Control, Robotics and Systems
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• v.10 no.4
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• pp.317-324
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• 2004

The ink jet printer media advance system is required to be exactly driven to the target position via tracking the reference velocity profile to obtain the high quality print image. A single gain PID controller is not sufficient to fulfill the control objectives, the exact velocity tracking and the accurate positioning, at the same time. A dual PID controller and its switching strategy are presented in this paper to achieve the control objectives. The media advance system is controlled by two separate PID controllers, one of which is for velocity control, and the other is for position control. A PID controller controls the velocity of the media advance system until it reaches the predetermined switching position. When the media advance system passes the predetermined position, the controller is switched to the other PID controller which is more profitable for exact positioning. The switching position is determined by the estimated stop distance. The simulation and experimental results are presented to show the validity and effectiveness of the proposed controller.

• The Transactions of the Korean Institute of Electrical Engineers D
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• v.50 no.3
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• pp.107-113
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• 2001

This paper deals with speed control of DC servo motor using a PID controller with a gain tuning based on a Back-Propagation(BP) Learning Algorithm. Conventionally a PID controller has been used in the industrial control. But a PID controller should produce suitable parameters for each system. Also, variables of the PID controller should be changed according to environments, disturbances and loads. In this paper described by a experiment that contained a method using a PID controller with a gain tuning based on a Back-Propagation(BP) Learning Algorithm, we developed speed characteristics of a DC servo motor on variable loads. The parameters of the controller are determined by neural network performed on on-line system after training the neural network on off-line system.

• Journal of Institute of Control, Robotics and Systems
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• v.3 no.6
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• pp.555-562
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• 1997

With only the classical PID controller applied to control of a DC motor, good (target) performance characteristic of the controller can be obtained if all the model parameters of DC motor and operating conditions such as external load torque, disturbance, etc. are known exactly. However, in case when some of system parameters or operating conditions are uncertain or unknown, the fixed PID controller does not guarantee good performance, which is assumed with precisely known system parameters and operating conditions. In view of this and the robustness enhancement of DC motor control system, we propose a PID learning controller which consists of a set of learning rules for PID gain tuning and learning of an auxiliary input. The proposed PID learning controller is shown to drive the state of uncertain DC motor system with unknown system parameters and external load torque to the desired one world wide asymptotically. Computer simulation and experimental results are given to demonstrate the effectiveness of the proposed PID learning controller, thereby showing its superiority to the conventional fixed PID controller.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.7 no.5
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• pp.830-836
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• 2006

In this paper described an auto tuning neuro-fuzzy PID controller based neural network. The PID type controller has been widely used in industrial application due to its simply control structure, easy of design, and inexpensive cost. However, control performance of the PID type controller suffers greatly from high uncertainty and nonlinearity of the system, large disturbances and so on. In this paper will design to take advantage of neural network fuzzy theory and pid controller auto toning technique. 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. This controller simple structure and computational complexity are less, and also application is easy and performance is excellent in system that is strong and has nonlinearity to system dynamic behaviour change or disturbance. Finally, the proposed auto tuning neuro-fuzzy controller is applied to magnetic levitation. Simulation results demonstrated that the control performance of the proposed controller is better than that of the conventional controller.

• 제어로봇시스템학회:학술대회논문집
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• 2001.10a
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• pp.45.1-45
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• 2001

This paper proposes a Mamdani fuzzy PID controller for controlling a process with small dead time. The controller composes of a parallel structure of fuzzy PI controller and fuzzy PD controller. Each controller has two inputs, error and change of error. Hence, the control signal of the proposed controller is the average value of the output of the fuzzy PI and PD controllers. The Mamdani fuzzy PID controller is easily to be adjusted to meet the desired control system performances both in transient state and steady state. The simulation results of the proposed Mamdani fuzzy PID controller by using the same parameters (proportional gain, integral time and derivative time) as the conventional PID controller are shown. The response of the Mamdani fuzzy PID control system is faster than the conventional PID control system. Both system responses have ...

• Journal of the Korea Institute of Information and Communication Engineering
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• v.18 no.8
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• pp.1811-1817
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• 2014

In this paper, a PID controller with interpolated gains by use of neural networks is proposed for the formation control problem that following robots track a leading robot with constant distances and angles when there are changes in the mass of the following robot. The whole control system is composed of a kinematic controller and a dynamic controller considering the robot dynamics. The dynamic controller is the PID controller with varying gains, and the proper gains are obtained for some representative masses of the follower robot by the genetic algorithm. Neural networks is trained using the genetic algorithm with the gain data obtained in the previous step. The trained neural network determines optimal PID gains for a random mass of following robot. Simulation studies show that for arbitrary masses of the tracking robot, the PID controller with interpolated gains by the trained neural network has better tracking performance than that of the PID controller with fixed gains.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.28 no.11C
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• pp.1062-1069
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• 2003

This paper presents a new loop shaping technique for design of robust optimal PID controllers in order to satisfy the performance requirements. PID controller can be designed by selecting the suitable weighting factors Q and R. This technique is developed by pushing all two zeros formed by PID controller closely to a larger pole of the second order plant. As a result, a good loop shaping is achieved in the high frequencies region on the Bode plot. For the robust optimal tuning of PID controller for second order system, a new loop shaping procedure is developed via LQR approach.

• Journal of the Semiconductor & Display Technology
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• v.1 no.1
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• pp.21-28
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• 2002

This paper presents a dynamic modeling and a robust PID controller design process for the wire bonder head assembly. For modeling elements, the system is divided into electrical part, magnetic part, and mechanical part. Each part is modeled using the bond graph method. The PID controller is used for high speed/high accuracy position control of the wire bonder assembly. The Taguchi method is used to obtain the more robust PID gain combinations than conventional one. This study makes use of an L18 array with three parameters varied on three levels. Results of simulations and experimental show that the designed PID controller provides a improved ratio of signal to noise and a reduced sensitivity improved to the conventional PID controller.

• Journal of the Korean Institute of Illuminating and Electrical Installation Engineers
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• v.22 no.9
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• pp.88-93
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• 2008

This paper proposes a frequency transfer function synthesis of a later compensated PID controller for an approximated low order model. The proposed method identifies the parameter vector of PID controller from a linear system that is formed by rearranging a loop frequency transfer function synthesis including the filter compensated PID controller obtained from the given frequency response bounds. And an example for the turbine speed control system of Chungju hydropower plant is given to illustrate the feasibilities of suggested schemes.

• Transactions of the Korean Society of Pressure Vessels and Piping
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• v.10 no.1
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• pp.1-6
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• 2014

PID(Proportional, Integral, Derivative) controller is one of the most used process controllers in nuclear power plants. The optimized parameter setting of process controller contributes to the stable operation and efficiency in the operating nuclear power plants. PID parameter setting is tuned when new process control system is established or process control system is changed. It is a burdensome work for I&C(Instrument and Control) engineers to tune the PID controller because it requires a lot of experience and knowledge. When the plant is in operation, inadequate PID parameter setting can be the cause of the unstable process of the plant. Therefore the results of PID parameter setting should be compared, simulated, verified and finally optimized. The practical PID tuning methods used in process controller are tuning operation calculation(Ziegler-Nicholes, Minimum TIAE, Lambda, IMC), exclusive tuning program based on computer and Matlab application. This paper introduces the various tuning methods and suggests an optimized PID tuning process in the operating nuclear power plants.