• Journal of Biosystems Engineering
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• 제31권3호
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• pp.203-208
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• 2006

This study was conducted to improve the control performance of a current variable-rate controller for granular fertilizers. Simulation model was developed. Optimized proportional, integral and derivative gains were determined by simulation model using 2nd order PID gain learning algorithm, and these control gains were evaluated through the field tests. Important results of this study are as follows; 1. Principles of pre-existing variable-rate application of granular fertilizers were investigated. 2. Simulation model of a PID controller that could simulate the control system was developed by using Matlab/Simulink program. The program was to determine PID control coefficients through the simulation model and 2nd order PID gain learning algorithm. 3. PID control coefficients obtained from the simulation were applied to the developed model. When the step input was given, Maximum overshoot were 1.96%, rise time were 0.05 sec, settling time were 0.06 sec and steady state error were 0.21 % respectively. 4. The simulation model was verified through field tests. The errors of maximum overshoot were 10%, rise time were 0.11 sec, settling time were 0.40 sec and steady state error were 8% because of loads and noises. Rise time was decreased to one third of that of the pre-existing system. 5. If the speed of a fertilizing machine is $0.3{\sim}0.6\;m/s$ and the maximum rotation speed of a discharging roller is 64 rpm, rise time would be 0.26 sec and fertilizing machine would cover the distance of $0.07{\sim}0.15\;m$ with settling time of 0.4 sec, fertilizing machine would cover the distance of $0.12{\sim}0.24\;m$.

• 제어로봇시스템학회:학술대회논문집
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• 제어로봇시스템학회 1996년도 한국자동제어학술회의논문집(국내학술편); 포항공과대학교, 포항; 24-26 Oct. 1996
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• pp.698-701
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• 1996

A novel neural network control scheme is proposed to identify the inverse dynamic model of robot manipulator and to compensate for uncertainties in robot dynamics. The proposed controller is called reference compensation technique(RCT) by compensating at reference input trajectory. The proposed RCT scheme has many benefits due to the differences in compensating position and learning algorithm. Since the compensation is done outside the plant it can be applied to many control systems without modifying the inside controller. It performs well with low controller gain because the operating range of input values is small and the output of the neural network controller is amplified through the controller gain. The back-propagation algorithm is used to train and simulations of three link robot manipulator are carried out to prove the proposed controller's performances.

• 한국정밀공학회지
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• 제21권5호
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• pp.64-72
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• 2004

An extension of interaction matrix formulation to the problem of system and disturbance identification for a plant that is corrupted by both process and output disturbances is presented. The teaming control develops controllers that learn to improve their performance at executing a given task, based on experience performing this task. The simplest forms of loaming control are based on the same concept as integral control, but operating in the domain of the repetitions of the task. This paper studies the use of such controllers in a decentralized system, such as a robot moving on the vertical plane with the controller for each link acting independently. The basic result of the paper is to show that stability and iterative precision of the learning controllers for all subsystems when the coupling between subsystems is turned off, assures stability of the decentralized teaming in the coupled system, provided that the sample time in the digital teaming controller is sufficiently short. The methods of teaming system are shown up for the iterative precision of each link.

• 로봇학회논문지
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• 제17권1호
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• pp.76-85
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• 2022

One of the most fundamental challenges when designing controllers for dynamic systems is the adjustment of controller parameters. Usually the system model is used to get the initial controller, but eventually the controller parameters must be manually adjusted in the real system to achieve the best performance. To avoid this manual tuning step, data-driven methods such as machine learning were used. Recently, reinforcement learning became one alternative of this problem to be considered as an agent learns policies in large state space with trial-and-error Markov Decision Process (MDP) which is widely used in the field of robotics. However, on initial training step, as an agent tries to explore to the new state space with random action and acts directly on the controller parameters in real systems, MDP can lead the system safety-critical system failures. Therefore, the issue of 'safe exploration' became important. In this paper we meet 'safe exploration' condition with Control Barrier Function (CBF) which converts direct constraints on the state space to the implicit constraint of the control inputs. Given an initial low-performance controller, it automatically optimizes the parameters of the control law while ensuring safety by the CBF so that the agent can learn how to predict and control unknown and often stochastic environments. Simulation results on a quadrotor UAV indicate that the proposed method can safely optimize controller parameters quickly and automatically.

• 조명전기설비학회논문지
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• 제23권2호
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• pp.103-107
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• 2009

PI제어기는 발전기의 전압제어 시스템에 널리 쓰이고 있다. 하지만 발전 시스템의 특성이 연속적으로 변화할 때, 새로운 PI매개변수를 결정하는 것이 쉽지 않다. 이를 해결하기 위하여 본 논문에서는 발전기의 전압제어에 신경망자율 적응 제어를 이용하는 제어 방법을 제안하였다. 전압제어 시스템의 적절한 연속적인 궤환 제어 이득은 델타학습 규칙에 의해서 결정된다. 제안된 제어 방법의 기능은 직류 발전기 전압제어 실험에 의해 확인하였다.

• 한국방송∙미디어공학회:학술대회논문집
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• 한국방송∙미디어공학회 2019년도 추계학술대회
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• pp.180-181
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• 2019

This paper proposes a rate control algorithm for intra coding frame in HEVC encoder using a deep learning approach. The proposed algorithm is designed for CTU level bit allocation in intra frame by considering visual features spatially and temporally. Our features are generated using visual geometry group (VGG-16) with deep convolutional layers, then it is used for bit allocation per each CTU within an intra frame. According to our experiments, the proposed algorithm can achieve -2.04% Luma component BD-rate gain with minimal bit accuracy loss against the HM-16.20 rate control model.

• 생명과학회지
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• 제6권3호
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• pp.178-184
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• 1996

Aloe(Aloe vera LINNE) has been used as a home medicine for the past several thousand in the world, and has been studied on various chronic degenerative diseases such as atherosclerosis, myocardiac infarction and hypertension. SMAP8, learning and memory impairment animal mode, were fed basic or experimental diets with 1.0% of freeze dried(FD)-Aloe powder for 8 months. This study was designed to investigate the effects of Aloe on body weight gain, grading score of senescence(GSS), triglyceride, total and LDL-cholesterol levels, and atherogenic index in serum of SAMP8, and also designed to investigate the effects of Aloe on cholesterol accumultions in mitochondria and microsome fractions of SAMP8 brain. Body weight gain was consistently lower in aloe group than in control group, but no significantly differences between them. Grading score of senescence resulted ina marked decreases pf 20% in 1.0% Aloe group compared with control group. Administrations of 1.0% aloe resulted ina marked decreases in 15% and 20% of triglyceride and cholesterol levels, respectively, and also significantly decreased in 15% of LDL-cholesterol levels and atherogenic index in serum of SAMP8 compared with control group. Cholesterol accumulations were significantly inhibited in 20% and 10% of mitochondria and microsome fractions of SAMP8 brain, respectively, by administration of 1.0% Aloe. These results suggest that administration of Aloe mau not only effectively inhibit chronic degenerative diseases in serum of SAMP8, but may also improve learning and memory impairments of SAMP8 brain.

• 대한의용생체공학회:의공학회지
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• 제11권1호
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• pp.83-88
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• 1990

A New speech processing algorithm using neural networks is proposed. We transform input data into frequency domain and process them by neural networks of 22 output neurons which have Bark scale on the ground that the Bark scale is similiar with that of the characteristics of human cochlea. An utilized neural network is multilayer perceptron, and the characteristics of cochlea have it trained by error back propagation learning algorithm. The trained neural networks suffices functions of human cochlea including the effects of automatic gain control, compression and equalization. Simulation results show that the proposed speech processing algorithm has good performance in automatic gain control, compression and equalization.

• 대한전기학회:학술대회논문집
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• 대한전기학회 1999년도 하계학술대회 논문집 F
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• pp.2610-2612
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• 1999

In controlling vector of induction motor, PID controller is required much time as the expert should control manually a gain of controller according to plant or a change of circumstances. Accordingly, this paper has gotten a gain of PID controller used neural network by self-funning method in order to settle above problem. The neural network can describe an input/output features in spite of non-linear system which is hard to get mathematical model by controlling the strength of connection by learning. It has a strong character against a distortion and noise of input information, and is suitable modeling of diver-variable system which is composed of several input/output. This paper has represented the self-tunning method for gain of PID controller used neural network when using PID controller to control speed of induction motor, and has checked strong characters against distortion and noise of input information through simulation.

• 제어로봇시스템학회논문지
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• 제9권4호
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• pp.277-283
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• 2003

Recently, near-field recording (NFR) disk drive schemes have been proposed with a view to increasing recording densities of hard disk drives. Compared with hard disk drives. NFR disk drives have narrower track pitches and are exposed to more severe periodic disturbances resulting from eccentric rotation of the disk. It is difficult to meet servo system design specifications for NFR disk drives with conventional VCM actuators in that the servo system for an NFR disk drive generally requires a feater gain and higher bandwidth. To tackle the problem various dual-stage actuator systems composed of a microactuator mounted on top of a conventional VCM actuator have been proposed. This article deals with the problem of designing a tracking servo system far an NFR disk drive adopting a dual-stage actuator. We summarize design constraints pertaining to the dual-stage servo system and present a new servo scheme using iterative teaming control. We design feedback compensators and an iterative teaming controller for a target plant and verify the validity of the proposed control scheme through a computer simulation.