• 한국산학기술학회:학술대회논문집
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• 한국산학기술학회 2010년도 추계학술발표논문집 1부
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• pp.341-343
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• 2010

The parameters of PID controller should be readjusted whenever system character change. In spite of a rapid development of control theory, this work needs much time and effort of expert. In this paper, to resolve this defect, after the sample of parameters in the changeable limits of system character is obtained, these parametrs are used as desired values of back propagation learning algorithm, also neural network auto tuner for PID controller is proposed by determing the optimum structure of neural network. Simulation results demonstrate that auto-tuning proper to system character can work well.

• 한국조명전기설비학회지:조명전기설비
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• 제9권6호
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• pp.71-77
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• 1995

This paper deals with design method of learning fuzzy controller for control of an unknown nonlinear plant using the self-tuning algorithm of fuzzy inference rules. In this method the fuzzy identification model obtained that the joined identification model of nonlinear part and linear identification model of linear part by fuzzy inference systems. This fuzzy identification model ordered self-tuning by Decent method so as to be servile to nonlinear plant. A the end, designed learning fuzzy controller of fuzzy identification model have learning structure to model reference adaptive system. The simulation results show that th suggested identification and learning control schemes are practically feasible and effective.

• 대한전기학회:학술대회논문집
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• 대한전기학회 1989년도 추계학술대회 논문집 학회본부
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• pp.426-429
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• 1989

The robust self-tuning controller is designed which is guaranteed the asymptotic regulation and tracking in the presence of a bounded parameter perturbation. The global stability in the presence of a finite noise and disturbance is ensured. The controller has a error driven structure, and involves the common model of a disturbance and reference input in the internal model. The adaptive system tunes the controller parameters such that the quadratic performance index which involves a weighting factor is optimized.

• 한국정밀공학회:학술대회논문집
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• 한국정밀공학회 1992년도 추계학술대회 논문집
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• pp.182-186
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• 1992

A self tuning technique is derived for PID controllers which are widely used in industries. The tuning algorithm is based upon a fuzzy indirect reasoning method and an iterative technique. The fuzzy technique is considered to obtain ease and simplicity of tuning process. The PID gains for the first tuning action are determined by a method which is modified from the Ziegler-Nichols step response method. The first PID gains are determined to obtain a control performance so close to a design performance that the followed tuning process can be made effectively. The design parameters are given as time-domain variables which human is familiar with. The results of simulation studies show that the proped tuning method can produce an effective tuning for arbitaray design performances.

• 한국소음진동공학회논문집
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• 제19권11호
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• pp.1103-1109
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• 2009

In this paper, the control of a membrane used in reverse osmosis desalination plant by using immune algorithm(IA) is addressed. The proposed algorithm IA of auto tuning method can find optimal gains and compared with conventional Ziegler-Nichols tuning method. The results of computer simulation represent that the proposed IA shows a good control performances better than Ziegler-Nichols tuning method.

• 한국지능시스템학회:학술대회논문집
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• 한국퍼지및지능시스템학회 2003년도 춘계 학술대회 학술발표 논문집
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• pp.325-329
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• 2003

This paper discuss the method of the system's efficient control using a Intelligent hybrid algorithm in nonlinear dynamics systems. Existing neural network and genetic algorithm for the control of non-linear systems work well in static states. but it be not particularly good in changeable states and must re-learn for the control of the system in the changed state. This time spend a lot of time. For the solution of this problem we suggest the intelligent hybrid self-tuning controller. it includes neural network, genetic algorithm and immune system. it is based on neural network, and immune system and genetic algorithm are added against a changed factor. We will call a change factor an antigen. When an antigen broke out, immune system come into action and genetic algorithm search an antibody. So the system is controled more stably and rapidly. Moreover, The Genetic algorithm use the memory address of the immune bank as a genetic factor. So it brings an advantage which the realization of a hardware easy.

• 융합신호처리학회 학술대회논문집
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• 한국신호처리시스템학회 2001년도 하계 학술대회 논문집(KISPS SUMMER CONFERENCE 2001
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• pp.121-124
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• 2001

In industrial actual control system, PID controller has been used with its high delicate control system in position control system. PID controller has simple structure and superior ability in several characteristics. When the response of system is changed by delay time, variable load , disturbances and external environment, control gain of PID controller must be readjusted on the system dynamic characteristics. Therefore, a control ability of PID controller is degraded when th control gain is inappropriately determined. When the response characteristic of system is changed under a condition, control gain of PID controller must be changed adaptively to be a waited response of system. In this paper an adaptive-tuning type PID controller is constructed by self-recurrent Neural Network(SRNN). applying back-propagation(BP) algorithm. Form the result of computer simulation in the proposed controller, its usefulness is verified.

• 대한전기학회:학술대회논문집
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• 대한전기학회 1998년도 하계학술대회 논문집 B
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• pp.509-511
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• 1998

Design of fuzzy logic controllers encounters difficulties in the selection of optimized membership function and fuzzy rule base, which is traditionally achieved by tedious trial-and-error process. In this paper We proposed a new method to generate fuzzy logic controllers throught genetic algorithm(GA). The controller design space is coded in base-7 strings chromosomes, where each bit gene matches the 7 discrete fuzzy value. The developed approach is subsequently applied to the design of proportional plus integral type fuzzy controller for a do-servo motor control system. It was presented in discrete fuzzy linguistic value, and used a membership function with Gaussian curve. The performance of this control system is demonstrated higher than that of a conventional PID controller and fuzzy logic controller(FLC).

• 대한전기학회:학술대회논문집
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• 대한전기학회 1997년도 하계학술대회 논문집 B
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• pp.609-611
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• 1997

In this paper, we propose the self-tuning of PID controller using diagonal recurrent neural networks. The characteristic of the proposed structure is on-line adaptive learning scheme in spite of variations of feedback, signals. Control performance is compared with that of neural network based PID controller which was proposed by Iwasa. Computer simulation results show that the proposed controller is effective in controlling of unknown nonlinear plants.

• 대한전기학회:학술대회논문집
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• 대한전기학회 1993년도 하계학술대회 논문집 A
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• pp.366-368
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• 1993

In this paper, we present an approach to automatically tune and adapt PID parameters by using Fuzzy Logic. PID controllers are well-known and found in many industries. Although the potentials of PID controllers, PID controllers are often poorly tuned and their capabilities are not fully used. We can think that the tuning of PID parameters is based on heuristics and some rule of thumbs. This is the reason we come to use Fuzzy Logic. We show that by imbedding heuristics and some rule of thumbs in PID controllers, reperesented by Fuzzy Logic. we can make PID controllers be robust to uncertainties such as load variations and adapt parameter changes.