• 제어로봇시스템학회:학술대회논문집
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• 1996.10b
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• pp.1392-1395
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• 1996

Nonlinear PID controllers have increasingly used in current industrial practice because it is robust and is easy to operate. Little guideline and tuning method, however, has been recommended for the nonlinear PID controllers while a lot of result is available for the linear PID controllers. Application guideline and tuning formulae are presented for error square type nonlinear controllers, which are most popular currently, are presented.

• Journal of the Korea Society of Computer and Information
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• v.24 no.1
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• pp.137-143
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• 2019

As the performance of computers increases, the use of deep learning, which has faced technical limitations in the past, is becoming more diverse. In many fields, deep learning has contributed to the creation of added value and used on the bases of more data as the application become more divers. The process for obtaining a better performance model will require a longer time than before, and therefore it will be necessary to find an optimal model that shows the best performance more quickly. In the artificial neural network modeling a tuning process that changes various elements of the neural network model is used to improve the model performance. Except Gride Search and Manual Search, which are widely used as tuning methods, most methodologies have been developed focusing on heuristic algorithms. The heuristic algorithm can get the results in a short time, but the results are likely to be the local optimal solution. Obtaining a global optimal solution eliminates the possibility of a local optimal solution. Although the Brute Force Method is commonly used to find the global optimal solution, it is not applicable because of an infinite number of hyper parameter combinations. In this paper, we use a statistical technique to reduce the number of possible cases, so that we can find the global optimal solution.

• Proceedings of the IEEK Conference
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• 2003.07c
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• pp.2493-2496
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• 2003

This paper presents an optimal tuning method of decentralized PID controller of two-input, two-output(TITO) second order system to be formulated as LQR.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.23 no.7
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• pp.1785-1796
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• 1998

This paper is concerned with the relationships between optimal number of wavelengths and tuning time in time division multiplexing(TDM) scheduling on wavelength division multiplexing(WDM) optical passive star networks. We assume that the traffic is nonuniform and each node has a tunable transmitter and a fixed receiver. Each node transmits spckets to all other nodes. Most of the earlier protocols on TDM based scheduling for WDM star networks [7, 8, etc.] use whold given wavelength chnnels. But in this paper, we investigate the optimal number of wavelengths that yidels minimum frame length when tuning time exists. It appears within the availble number of wavelengths that yields minimum frame length when tuning time exists. It appears within the available number of wavelengths. We analyze the relationships between optimal number of wavelengths and tuning time by experiments. We analyze the reationships between optimal number of wavelengths and tuning time by experiments. We also discuss on the possibility of reduction of frame length by increasing the number of nodes trans-mitters and receivers.

• Proceedings of the KIEE Conference
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• 2005.05a
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• pp.87-89
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• 2005

This paper presents an optimal tuning method of the decentralized PI controller of the two-input, two-output(TITO) second order system with time-delay using LQR approach.

• 제어로봇시스템학회:학술대회논문집
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• 1996.10b
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• pp.295-298
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• 1996

In this paper, a self-tuning optimal control algorithm is proposed to retain the optimal performance of an active suspension system, when the vehicle has some time varying parameters and parameter uncertainties. We consider a 2 DOF time-varying quarter car model which has the parameter variation of sprung mass, suspension spring constant and suspension damping constant. Instead of solving algebraic riccati equation on line, we propose a neural network approach as an alternative. The optimal feedback gains obtained from the off line computation, according to parameter variations, are used as the neural network training data. When the active suspension system is on, the parameters are identified by the recursive least square method and the trained neural network controller designer finds the proper optimal feedback gains. The simulation results are represented and discussed.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.57 no.6
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• pp.915-923
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• 2008

This paper focuses on optimal tuning of nonlinear parameters of a dual-input power system stabilizer(dual-input PSS), which can improve the system damping performance immediately following a large disturbance. Until recently, various PSS models have developed to bring stability and reliability to power systems, and some of these models are used in industry applications. However, due to non-smooth nonlinearities from the interaction between linear parameters(gains and time constants of linear controllers) and nonlinear parameters(saturation output limits), the output limit parameters cannot be determined by the conventional tuning methods based on linear analysis. Only ad hoc tuning procedures('trial and error' approach) have been used. Therefore, the steepest descent method is applied to implement the optimal tuning of the nonlinear parameters of the dual-input PSS. The gradient required in this optimization technique can be computed from trajectory sensitivities in hybrid system modeling with the differential-algebraic-impulsive-switched(DAIS) structure. The optimal output limits of the dual-input PSS are evaluated by time-domain simulation in both a single machine infinite bus(SMIB) system and a multi-machine power system in comparison with those of a single-input PSS.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 2003.09a
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• pp.358-363
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• 2003

Optimal tuning plays an important role in operations or tuning of the complex process such as the main steam temperature of the thermal power plant. However, it is very difficult to maintain the steam temperature of power plant using conventional optimization methods, since these processes have the time delay and the change of the dynamic characteristics in the reheater. Up to the present time, the Pm controller has been used. However, it is not easy 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 suggests immune algorithm based tuning technique for PID Controller on steam temperature process with long dead time and its results are compared with genetic algorithm based tuning technique.

• Proceedings of the IEEK Conference
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• 2001.06e
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• pp.21-24
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• 2001

This paper presents a loop shaping procedure for tuning optimal and robust PID controller by selecting the appropriate weighting factors Q and R in order to satisfy such the performance requirements.

• International Journal of Fuzzy Logic and Intelligent Systems
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• v.1 no.1
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• pp.95-103
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• 2001

The purpose of Introducing a combined cycle with gas turbine in power plants is to reduce losses of energy, by effectively using exhaust gases from the gas turbine to produce additional electricity or process. The efficiency of a combined power plant with the gas turbine increases, exceeding 50%, while the efficiency of traditional steam turbine plants is approximately 35% to 40%. Up to the present time, the PID controller has been used to operate this system. However, it is very difficult to achieve an optimal PID gain without any experience, since the gain of the PID controller has to be manually tuned by trial and error procedures. This paper focuses on the neural network tuning of the 2-DOF PID controller with a combined 2-DOF parameter (NN-Tuning 2-DOF PID controller), for optimal control of the Gun-san gas turbine generating plant in Seoul, Korea. In order to attain optimal control, transfer function and operating data from start-up, running, and stop procedures of the Gun-san gas turbine have been acquired and a designed controller has been applied to this system. The results of the NN-Tuning 2-DOF PID are compared with the PID controller and the conventional 2-DOF PID controller tuned by the Ziegler-Nichols method through experimentation. The experimental results of the NN-Tuning 2-DOF PID controller represent a more satisfactory response than those of the previously-mentioned two controllers.