• 제어로봇시스템학회:학술대회논문집
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• 2000.10a
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• pp.505-505
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• 2000

Controlling the process by PID controller is widely used in industry by applying Ziegler-Nichols method in analyzing parameter of the controller. However, in fact. it is still necessary to tune parameter in order to obtain the best process response. This paper presents a Self-Tuning PID controller utilizes the personal computer to synthesize and analyze controller parameter as well as tune for appropriate parameter by using Dahlin method and Extrapolation. Experimental results using a Self-Tuning PID controller to control water level and temperature, it is found that the controller being developed is able to control the process very effectively and provides a good response similar to the controller used in the industry.

• Proceedings of the KIEE Conference
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• 2004.11c
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• pp.62-65
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• 2004

An optimal robust controller design method for gun driving system is discussed in this paper. The parameters of the gun driving controller are tuned by using the LQR characteristics for the performance and robustness. Tuning method that optimize velocity error gives a significant improvement over the existing PID tuning methods. It is shown that the tuning result of real gun driving system which is regarded as rigidness model or stiffness model satisfy performance and robustness.

• Journal of the Korean Institute of Telematics and Electronics B
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• v.30B no.11
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• pp.27-36
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• 1993

It is well know that the tuning method of Yuwana-Seborg (1982) can not be even applied to the relatively small delay system due to the phase errors from the Pad approximation suh (1984, 85, 86). Jutan and Rodriguez(1984), lee(1989), Chen(1989), Lee and Edgar(1990), etc have been tried to improve it. In this stydy, such recent results and their problems are investigated. A new tuning method by "a frequency scale transformation". which is based on a previous result of Suh(1984). is proposed. It can reduce the magnitude of the phase error significantly.

• Proceedings of the KIEE Conference
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• 1992.07a
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• pp.476-479
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• 1992

This paper presents a neural network method for tuning PlD controller of a time-varying process. Three gains of PlD controller are tuned for a certain desirable response pattern by back-propagation neural network. The neural network is trained using changes of output features vs. changes of PlD gains. But sometimes it needs longer training time and larger structure to train the correlation between the process and controller on entire region of the process. The difficulty in system identification is that the inverse function of the system can not be clearly stated. To cope with the problem, we do not train the neural network to respond correctly for the entire regions but train for only local region where the system is heading toward by training the neural network and tuning of the PlD controller. It may be trained for fine-tuning itself. Simulation results show that the adaptive PID controller using neural network trained in the local area performs remarkably for time-varying second order process.

• Journal of the Institute of Electronics Engineers of Korea SC
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• v.39 no.4
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• pp.1-5
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• 2002

This paper presents a new loop shaping procedure for tuning the robust optimal LQ-PID regulators by selecting the appropriate weighting factors Q and R in order to satisfy both the stability-robustness and the performance-robustness.

• Journal of Electrical Engineering and Technology
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• v.10 no.3
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• pp.1244-1254
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• 2015

This paper proposes a new method for tuning a linear quadratic - proportional integral derivative controller for second order systems to simultaneously meet the time and frequency domain design specifications. The suitable loop-shape of the controlled system and the desired step response are considered as specifications in the time and frequency domains, respectively. The weighting factors, Q and R of the LQ controller are determined by the algebraic Riccati equation with respect to the limiting behavior and target function matching. Numerical examples show the effectiveness of the proposed LQ-PID tuning method

• Journal of the Korean Society for Precision Engineering
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• v.24 no.12
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• pp.57-64
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• 2007

There have been many researches for optimal controllers in multivariable systems, and they generally use accurate linear models of the plant dynamics. Real systems, however, contain nonlinearities and high-order dynamics that may be difficult to model using conventional techniques. Therefore, it is necessary a PID gain tuning method without explicit modeling for the multivariable plant dynamics. The PID tuning method utilizes the sign of Jacobian and gradient descent techniques to iteratively reduce the error-related objective function. This paper, especially, focuses on the role of I-controller when there is a steady state error. However, it is not easy to tune I-gain unlike P- and D-gain because I-controller is mainly operated in the steady state. Simulations for an overhead crane system with dynamic friction show that the proposed PID-LC algorithm improves controller performance, even in the steady state error.

• Membrane and Water Treatment
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• v.9 no.2
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• pp.129-136
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• 2018

In this contribution, the control of multivariable reverse osmosis (RO) desalination plant using proportional-integral-derivative (PID) controllers is presented. First, feed-forward compensators are designed using simplified decoupling method and then the PID controllers are tuned for flux (flow-rate) and conductivity (salinity). The tuning of PID controllers is accomplished by minimization of the integral of squared error (ISE). The ISEs are minimized using a recently proposed algorithm named as teacher-learner-based-optimization (TLBO). TLBO algorithm is used due to being simple and being free from algorithm-specific parameters. A comparative analysis is carried out to prove the supremacy of TLBO algorithm over other state-of-art algorithms like particle swarm optimization (PSO), artificial bee colony (ABC) and differential evolution (DE). The simulation results and comparisons show that the purposed method performs better in terms of performance and can successfully be applied for tuning of PID controllers for RO desalination plants.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.60 no.9
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• pp.1741-1747
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• 2011

Though various techniques have been studied as a way of adjusting parameters of PID controllers, no perfect method of determining parameters is available to date. This paper proposes a new method for enhancing performance of PID controllers by using the characteristics of dual-input describing function (DIDF). In other words, if nonlinear elements with two inputs (DIDF) are connected in series to the plant, the critical point (-1+j0) for Nyquist stability theory can be moved to a position arbitrarily selected on the complex plane by determining necessary coefficients of the DIDF appropriately. This makes the application of the existing conventional PID parameter tuning methods a lot easier, and stability and robustness of the system are improved simultaneously due to the DIDF inserted.

• The Proceedings of the Korean Institute of Illuminating and Electrical Installation Engineers
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• v.13 no.2
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• pp.195-195
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• 1999

This paper is Proposed a new method to deal with the optimized auto-tuning for the PID controller which is used to the process-control in various fields. First of all, in this method, 1st order delay system with dead time which is modelled from the unit step response of the system is Pade-approximated, then initial values are determined by the Ziegler-Nickels method. So we can find the parameters of PID controller so as to minimize the fuzzy criterion function which includes the maximum overshoot, damping ratio, rising time and settling time. Finally, after studying the parameters of PID controller by Backpropagation of Neural-Network, when we give new K, L, T values to Neural-Network, the optimized parameter of PID controller is found by Neural-Network Program.