• 대한전기학회:학술대회논문집
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• 대한전기학회 2002년도 하계학술대회 논문집 B
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• pp.1165-1168
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• 2002

This paper presents a neural network based self-tuning fuzzy PID control scheme with variable learning rate for sensorless vector controlled induction motor drives. MRAS(Model Reference Adaptive System) is used for rotor speed estimation. When induction motor is continuously used long time. its electrical and mechanical parameters will change, which degrade the performance of PID controller considerably. This paper re-analyzes the fuzzy controller as conventional PID controller structure, introduces a single neuron with a back-propagation learning algorithm to tune the control parameters, and proposes a variable learning rate to improve the control performance. The proposed scheme is simple in structure and computational burden is small. The simulation using Matlab/Simulink and the experiment using DS1102 board show the robustness of the proposed controller to parameter variations.

• 대한전기학회논문지
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• 제37권9호
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• pp.646-654
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• 1988

In this paper, a speed controller using a microcomputer is implemented and applied to a DC Servo Motor. Adaptive control is applied to a system for which a priori knowledge to its mathematical model is insufficient, on the basis of input and output data an apropriate controller is constructed through which the system input is synthesized. The pole-placement PID self tuning control algorithms as a control algorithm is used to compare the performance of the controller with that of the classical PID controller through computer simulations and experiments.

• International Journal of Automotive Technology
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• 제4권4호
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• pp.181-191
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• 2003

Since the nonlinearity and uncertainties which inherently exist in vehicle system need to be considered in active suspension control law design, this paper proposes a new control strategy for active vehicle suspension systems by using a combined control scheme, i.e., respectively using a genetic algorithm (GA) based self-tuning PID controller and a fuzzy logic controller in two loops. In the control scheme, the PID controller is used to minimize vehicle body vertical acceleration, the fuzzy logic controller is to minimize pitch acceleration and meanwhile to attenuate vehicle body vertical acceleration further by tuning weighting factors. In order to improve the adaptability to the changes of plant parameters, based on the defined objectives, a genetic algorithm is introduced to tune the parameters of PID controller, the scaling factors, the gain values and the membership functions of fuzzy logic controller on-line. Taking a four degree-of-freedom nonlinear vehicle model as example, the proposed control scheme is applied and the simulations are carried out in different road disturbance input conditions. Simulation results show that the present control scheme is very effective in reducing peak values of vehicle body accelerations, especially within the most sensitive frequency range of human response, and in attenuating the excessive dynamic tire load to enhance road holding performance. The stability and adaptability are also showed even when the system is subject to severe road conditions, such as a pothole, an obstacle or a step input. Compared with conventional passive suspensions and the active vehicle suspension systems by using, e.g., linear fuzzy logic control, the combined PID and fuzzy control without parameters self-tuning, the new proposed control system with GA-based self-learning ability can improve vehicle ride comfort performance significantly and offer better system robustness.

• 대한전자공학회논문지
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• 제27권2호
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• pp.55-64
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• 1990

직류 전동기의 위치 및 속도 제어를 위해 그동안 주로 사용해 왔던 고전적인 선형 PID 제어 알고리듬을 사용하여 왔으나, 주위환경의 변화나 부하의 변경 또는 외란과 같은 비션형 요소들로 인해 실제 시스템의 모델링에는 많은 제한이 따랐다. 이 문제를 해결하기 위해 시스템의 모델링 없이 매개변수들을 온라인으로 추정하여 식별할 수 있는 PID 자기 동조기의 설계 방법을 제안하였다. 본 논문에서는 극 배치 PID 자기 동조 제어기의 설계 기법을 제안하고, 각각의 제어기 매개변수들을 추정하기 위하여 순환 최소자승 알고리듬을 사용하야T으며, Diophantine 방정식의 도입으로 인한 4개의 추가매개 변수들을 추정된 제어기 매개변수들을 이용하여 새롭게 유도된 방정식에서 구하였다. 제안된 제어기의 성능을 평가하기 위하여 최소, 비최소 위상 시스템에 대한 시뮬레이션을 하였고 실제 로보트 매니플레이터용 DC 서어보 전동기에 대하여 무부하와 부하 실험을 거쳐 그 특성이 양호함을 검증하였다.

• 제어로봇시스템학회논문지
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• 제22권11호
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• pp.925-931
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• 2016

The BLDC motor is used widely in industry due to its controllability and freedom from maintenance because there is no mechanical brush in the BLDC motor. Furthermore, it is suitable for high-speed applications, such as compressors and air blowers. For instance, for a compressor with a small impeller due to miniaturizing, the BLDC motor has to rotate at a very high speed to maintain the compression ratio of the compressor. Typically, to reach an ultra-high speed, airfoil bearings must be used in place of ball bearings because of their friction. Unfortunately, the characteristics of airfoil bearings change drastically depending on the revolution speed. In this paper, a BLDC motor with airfoil bearings is controlled with a PID controller. To analyze and determine the PID coefficients, the relay-feedback method is used. Additionally, for adaptive control, a fuzzy logic controller is used. Furthermore, the auto-tuning and self-tuning techniques are combined to control the BLDC motor. The proposed method is able to control the airfoil-bearing BLDC motor efficiently.

• 대한전기학회:학술대회논문집
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• 대한전기학회 1984년도 하계학술회의논문집
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• pp.33-35
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• 1984

• 대한전기학회:학술대회논문집
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• 대한전기학회 1992년도 하계학술대회 논문집 A
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• pp.333-336
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• 1992

In this paper, We propose a PID-type of self-tuning algorithm which is based on the parameter estimation and the minimization of the cost function. We use the CARIMA model for parameter estimation and determine the discrete PID controller parameters by minimizing the cost function which considers the quadratic deviations of the predicted output over the set-point as well as the control efforts. Also, The algorithm is extended by incorporating constraints of the control signal. Simulations are performed to illustrate the efficiency of the proposed method.

• 대한기계학회논문집A
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• 제27권9호
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• pp.1463-1468
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• 2003

This paper presented the real-time self-tuning learning control based on evolutionary computation, which proves its superiority in finding of the optimal solution at the off-line learning method. The individuals of the populations are reduced in order to learn the evolutionary strategy in real-time, and new method that guarantee the convergence of evolutionary mutations is proposed. It is possible to control the control object slightly varied as time changes. As the state value of the control object is generated, evolutionary strategy is applied each sampling time because the learning process of an estimation, selection, mutation is done in real-time. These algorithms can be applied; the people who do not have knowledge about the technical tuning of dynamic systems could design the controller or problems in which the characteristics of the system dynamics are slightly varied as time changes.

• 전자공학회논문지SC
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• 제39권6호
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• pp.1-8
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• 2002

본 논문에서는 시간지연이 존재하고 시스템의 영점이 단위원 밖에 있으며 시스템 파라미터가 변하는 비선형 시스템에 적응하는 신경회로망을 이용한 PID구조를 갖는 일반화 최소분산 자기동조제어기를 제안한다. 신경회로망은 제어기 파라미터를 추정하며 제어 출력은 추정된 제어기 파라미터로부터 얻어진다. 제어 알고리듬의 타당성을 확인하기 위해 시간 지연이 있고 일정한 시간이 경과한 후 시스템의 파라미터가 변하는 비선형 비최소위상 시스템에 대해 컴퓨터 시뮬레이션을 하였다. 그리고 신경회로망을 이용한 직접 적응 제어기와 비교하였다.

• 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.