• 제어로봇시스템학회:학술대회논문집
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• 2000.10a
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• pp.348-348
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• 2000

In this paper, we present a fuzzy moving sliding mode control for two-degrree-of-freedom robotic manipulator. 17he sliding surface parameters are designed by fuzzy inference. The proposed sliding mode control makes the error always remain on the surface from beginning and therefore, the system is insensitive to system uncertaintics and external disturbances. Simulation results show the effectiveness of proposed scheme.

• Journal of the Korean Institute of Intelligent Systems
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• v.25 no.4
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• pp.319-326
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• 2015

Recently in the fields o f stochastic optimal control ( SOC) and reinforcemnet l earning (RL), there have been a great deal of research efforts for the problem of finding data-based sub-optimal control policies. The conventional theory for finding optimal controllers via the value-function-based dynamic programming was established for solving the stochastic optimal control problems with solid theoretical background. However, they can be successfully applied only to extremely simple cases. Hence, the data-based modern approach, which tries to find sub-optimal solutions utilizing relevant data such as the state-transition and reward signals instead of rigorous mathematical analyses, is particularly attractive to practical applications. In this paper, we consider a couple of methods combining the modern SOC strategies and approximate inference together with machine-learning-based data treatment methods. Also, we apply the resultant methods to a variety of application domains including financial engineering, and observe their performance.

• Journal of Bio-Environment Control
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• v.20 no.3
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• pp.169-175
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• 2011

The adaptive neuro-fuzzy inference system (ANFIS) based automatic control system framework was proposed for integrated environment management of ubiquitous plant factory which can collect information of crop cultivation environment and monitor it in real-time by using various environment sensors. Installed wireless sensor nodes, based on the sensor network, collect the growing condition's information such as temperature, humidity, $CO_2$, and the control system is to monitor the control devices by using ANFIS. The proposed automatic control system provides that users can control all equipments installed on the plant factory directly or remotely and the equipments can be controlled automatically when the measured values such as temperature, humidity, $CO_2$, and illuminance deviated from the decent criteria. In addition, the better quality of the agricultural products can be gained through the proposed automatic control system for plant factory.

• Journal of Power Electronics
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• v.17 no.6
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• pp.1587-1599
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• 2017

This paper proposes a real-time implementation of an optimal operation of a double stage grid connected wind power system incorporating an active power filter (APF). The system is used to supply the nonlinear loads with harmonics and reactive power compensation. On the generator side, a new adaptive neuro fuzzy inference system (ANFIS) based maximum power point tracking (MPPT) control is proposed to track the maximum wind power point regardless of wind speed fluctuations. Whereas on the grid side, a modified predictive current control (PCC) algorithm is used to control the APF, and allow to ensure both compensating harmonic currents and injecting the generated power into the grid. Also a type 2 fuzzy logic controller is used to control the DC-link capacitor in order to improve the dynamic response of the APF, and to ensure a well-smoothed DC-Link capacitor voltage. The gained benefits from these proposed control algorithms are the main contribution in this work. The proposed control scheme is implemented on a small-scale wind energy conversion system (WECS) controlled by a dSPACE 1104 card. Experimental results show that the proposed T2FLC maintains the DC-Link capacitor voltage within the limit for injecting the power into the grid. In addition, the PCC of the APF guarantees a flexible settlement of real power exchanges from the WECS to the grid with a high power factor operation.

• Journal of Institute of Control, Robotics and Systems
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• v.7 no.8
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• pp.664-674
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• 2001

In this paper, as a new category of fuzzy-neural networks architecture, we propose Fuzzy Polynomial Neural Networks (FPNN) and discuss a comprehensive design methodology related to its architecture. FPNN dwells on the ideas of fuzzy rule-based computing and neural networks. The FPNN architecture consists of layers with activation nodes based on fuzzy inference rules. Here each activation node is presented as Fuzzy Polynomial Neuron(FPN). The conclusion part of the rules, especially the regression polynomial, uses several types of high-order polynomials such as linear, quadratic and modified quadratic. As the premise part of the rules, both triangular and Gaussian-like membership functions are studied. It is worth stressing that the number of the layers and the nods in each layer of the FPNN are not predetermined, unlike in the case of the popular multilayer perceptron structure, but these are generated in a dynamic manner. With the aid of two representative time series process data, a detailed design procedure is discussed, and the stability is introduced as a measure of stability of the model for the comparative analysis of various architectures.

• Journal of the Korean Institute of Intelligent Systems
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• v.10 no.5
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• pp.401-408
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• 2000

In this paper, we proposed a fuzzy controller that founded by the general feedback control with the new adjustment method when it's tuning. The general feedback controller is operated that supply to the plant making the control input multiplying the appropriate gain of controller on the error between the output of the plant and the reference, But proposed feedback fuzzy controller consist of three loops. The inner loop consists of plant and an ordinary feedback controller. The fuzzy inference of controller performed by the outer loops, which is composed of a fuzzy modeling and inference. We can observe that the output of control system converges toward the reference. Also, the behaviour of feedback fuzzy system is converged from the transient. That is, we verified that designed fuzzy controllers was adapted effectively through the experiments in the hydraulic motor system using floating point DSP processor.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.15 no.2
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• pp.181-187
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• 2015

In this study, an LED emotional lighting algorithm optimized to the situation was implemented using fuzzy inference for users in exercise and resting situations in indoor environment. The fuzzy theory was used instead of the conventional simple color temperature control in order to control the colors and color temperatures of LED light sources in line with user environment under complex conditions. An LED emotional lighting system based on fuzzy theory was designed through a combination of colors according to the color and temperature of emotional language based on the user behavior. As a result, the color and color temperature can give a good effect on user's emotions for an emotional lighting that is effective for resting and exercise.

• Proceedings of the IEEK Conference
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• 1998.10a
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• pp.433-436
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• 1998

The objective of this paper is to design a adaptive fuzzy controller for autonomous navigation of mobile robot. The adaptive fuzzy controller has an advantage in data processing time and convergence speed. The basic idea of control is to induct membership function and fuzzy inference rules and to scale inducted membership function to suitable robot state. The adaptive fuzzy control method is applied to mobile robot and the simulation results show the effectiveness of our controller.

• 제어로봇시스템학회:학술대회논문집
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• 1991.10a
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• pp.878-883
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• 1991

In this paper, we propose the fuzzy-neural controller which is fuzzy controller with learning ability of neural network. The neural network in this controller is same as the membership function in current fuzzy controller and a parts of inference rules. And, it can be easily extend the control algorithm to multivariable systems. We can show effectiveness of the control algorithm through experiment of the inverted pendulum system.

• Proceedings of the Korean Society of Machine Tool Engineers Conference
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• 1998.10a
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• pp.54-59
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• 1998

In order to minimize straightness error of deflected shafts, a automatically bending process control system is designed, fabricated, and studied. The multi-step straightening process and the three-point bending process are developed for the geometric adaptive straightness control. Load-deflection relationship, on-line identification of variations of material properties, on-line springback prediction, and studied for the three-point bending processes. Selection of a loading point supporting condition are derved form fuzzy inference and fuzzy self-learning method in the multi-step straighternign process. Automatic straightening machine is fabricated by using the develped ideas. Validity of the proposed system si verified through experiments.