• Proceedings of the KIEE Conference
• /
• 1992.07a
• /
• pp.337-339
• /
• 1992

This paper deals with the tuning method of PID controls for process controls. It introduces the normalized process gain and the normalized process dead-time for processes based on Ziegler-Nichols tuning methods. In the case of PID auto-tuning, the first, this method applies Ziegler-Nichols tuning method and introduces the set-point weighting for reducing overshoot in the large normalized process gain or small normalized process dead-time, the second, this method is modified and includes the set-point weighting in the small normalized process gain or large normalized process dead-time. In the case of PI auto-tuning, this method is modified for reducing overshoot. This paper obtains empirical data with Ziegler-Nichols methods for refined Ziegler-Nichols tuning methods.

• Journal of the Korean Institute of Illuminating and Electrical Installation Engineers
• /
• v.16 no.2
• /
• pp.54-58
• /
• 2002

PID Controller is widely used as automatic equipment for industry. However, when a system has various characters, parameter decision and tuning for accurate control is a hard task. In this paper, expert auto-tuning PID controller using PLC is presented as away of solving this problem. Expert auto tuning algorithm is based on Ziegler-Nichols step response and expert knowledge. The test of control performance is carried out in practical speed control of Induction Motor in variable load, the experimental results suggest its superior performance.

• Journal of Institute of Control, Robotics and Systems
• /
• v.15 no.10
• /
• pp.974-981
• /
• 2009

In this paper, a method of auto-tuning of PID (Proportional-Integral-Derivative) and PIDA (Proportional-Integral-Derivative-Acceleration) controllers is proposed that can be applied to a time-delayed second order model. The proposed identification method is based on step responses, but it can be easily automated rising digital controller unlike the existing graphical identification methods. We provide a ways to yield parameter identifications which is independent to initial values of the plants. The tuning rule is based on the pole-placement strategy and is formulated so that it can be implemented using a digital controller with ease.

• Proceedings of the KIEE Conference
• /
• 1999.07b
• /
• pp.504-506
• /
• 1999

In this paper, we propose a method which control parametric uncertainty systems using PID controller by fuzzy auto tuning. We get the error and the error change rate of plant output correspond to the initial value of parameter using the Ziegler-Nickols tuning and determine the new proportional gain$(K_p)$ and the integral time $(T_i)$ from fuzzy tuner by the error and error change rate of plant output as a membership function of fuzzy theory. The Fuzzy Auto-tuning algorithm for PID controller operate to adapt variable parameter of plant in parametric uncertainty systems. It is shown this method considerably improve the transient response at computer simulation.

• 제어로봇시스템학회:학술대회논문집
• /
• 2000.10a
• /
• pp.109-109
• /
• 2000

In process industries, more than 90% of the control loops have PID controller. Futhermore, the most control systems are using classical PID controllers for their process control. Various auto-tuning methods of PID gains using relay-feedback are presented recently. In order to get the desired control performance, the correct tuning of PID controller is very important. This paper suggests how to tune of digital PID gains using information for both the Nyquist critical point by conventional method and another point by the relay feedback and hidden time-delay term. Simulation results show that the proposed controller has better performance than the conventional method.

• Proceedings of the Korean Institute of Intelligent Systems Conference
• /
• 1993.06a
• /
• pp.964-967
• /
• 1993

This paper presents an auto tuning method of fuzzy inference using Genetic Algorithm. The determination of membership functions by human experts is a difficult problem. Therefore, some auto-tuning methods have been proposed to reduce the time-consuming operations. However, the convergence of the tuning by the previous methods depends on the initial conditions of the fuzzy model. So, we proposes an auto tuning method for the fuzzy neural network by Genetic Algorithm (ATF system). This paper shows effectiveness of the ATF system by simulations.

• Journal of Satellite, Information and Communications
• /
• v.11 no.4
• /
• pp.39-43
• /
• 2016

In this paper, we propose PID gain auto tuning method in steering type mobile robot. PID controller gain select method are various methods. Ziegler-Nichols step tuning method is one method tuning in PID controller. Use step tuning method find a the first gain and did experiment in steering mobile robot. and Make a new the second gains from the first gains. After appling the second gain in PID controller, Where perform observe for convergence time and stabilization error. Experiments result the second gain are useful in real steering mobile robot system.

• Proceedings of the Korean Society of Precision Engineering Conference
• /
• 2007.11a
• /
• pp.129-130
• /
• 2007

• Proceedings of the KIPE Conference
• /
• 2003.07b
• /
• pp.994-997
• /
• 2003

Simple tuning formulae are derived to design a Pl controller to meet the gain and phase margin specifications. These formulae are suitable for the auto-tuning of a process where the robustness should be guaranteed. The auto-tuned PI controller is examined for the speed regulation of a PM synchronous motor.

• Proceedings of the KIEE Conference
• /
• 1990.07a
• /
• pp.510-513
• /
• 1990

This paper describes the procedures for pre-tuning and re-tuning of the PI controller to specifications on patterns of output response. The key ideas of the proposed adaptive scheme are as follows. The relay feedback is adopted first for pre-tuning and the adaptive algorithms by the pattern recognition are introduced for re-tuning procedure to retune the gains whenever control conditions are changed. The proposed scheme was applied to the experimental laboratory process, heat exchanger.