• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 1996.10a
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• pp.172-175
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• 1996

In the paper is proposed a hierarchical self-learning fuzzy controller for balancing and position control of an circular inverted pendulum system. To stabilize the pendulum at a specified position, the hierarchical fuzzy controller consists of a supervisory controller, a self-learning fuzzy controller, and a forced disturbance generator. Simulation example shows the effectiveness of the proposed method.

• 제어로봇시스템학회:학술대회논문집
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• 1997.10a
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• pp.516-519
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• 1997

In this paper, the control of the temperature for the vehicle air conditioner is implemented with the fuzzy controller using a micro controller. The linguistic control rules of the fuzzy controller are separated into two out variables(multi input multi output ; MIMO) : one is those for the blower motor, and the other is those for air mix door. The error in fuzzy controller, the input variable is defined as difference between the reference temperature and the actual temperature in the cabin room. The fuzzy control rules are established from the human operator experience, and based engineering knowledge about the process. The method of the center of gravity is utilized for the defuzzification.

• Transactions on Control, Automation and Systems Engineering
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• v.1 no.2
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• pp.147-152
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• 1999

It is an interesting area in the field of artifical intelligence to find an analytic model of cooperative structure for multiagent system accomplishing a given task. Usually it is difficult to design controllers for multi-agent systems without a comprehensive knowledge about the system. One of the way to overcome this limitation is to implement an evolutionary approach to design the controllers. This paper introduces the use of a genetic algorithm to discover a fuzzy logic controller with rules that govern emergent agents solving a pursuit problem in a continuous world. Simulation results indicate that, given the complexity of the problem, an evolutionary approach to find the fuzzy logic controller seems to be promising.

• International Journal of Control, Automation, and Systems
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• v.3 no.2
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• pp.217-224
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• 2005

Generally, the underwater flight vehicle (UFV) depth control system operates with the following problems: it is a multi-input multi-output (MIMO) system because the UFV contains both pitch and depth angle variables as well as multiple control planes, it requires robustness because of the possibility that it may encounter uncertainties such as parameter variations and disturbances, it requires a continuous control input because the system that has reduced power consumption and acoustic noise is more practical, and further, it has the speed dependency of controller parameters because the control forces of control planes depend on the operating speed. To solve these problems, an adaptive fuzzy sliding mode controller (AFSMC), which is based on the decomposition method using expert knowledge in the UFV depth control and utilizes a fuzzy basis function expansion (FBFE) and a proportional integral augmented sliding signal, is proposed. To verify the performance of the AFSMC, UFV depth control is performed. Simulation results show that the AFSMC solves all problems experienced in the UFV depth control system online.

• Journal of Advanced Marine Engineering and Technology
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• v.28 no.2
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• pp.228-236
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• 2004

In this paper. we present a neuro-fuzzy controller which unifies both fuzzy logic and multi-layered feed forward neural networks. Fuzzy logic provides a means for converting linguistic control knowledge into control actions. On the other hand. feed forward neural networks provide salient features. such as learning and parallelism. In the proposed neuro-fuzzy controller. the parameters of membership functions in the antecedent part of fuzzy inference rules are identified by using the error back propagation algorithm as a learning rule. while the coefficients of the linear combination of input variables in the consequent part are determined by using the least square estimation method. Finally. the effectiveness of the proposed controller is verified through computer simulation for an inverted pole system.

• Proceedings of the KIEE Conference
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• 1996.07b
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• pp.1092-1095
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• 1996

In this paper The tuning of PID controller for multi input-output is studied by using fuzzy inference. State of coupling is estimated by fuzzy inference, its results is used for tuning of PID controller to get optimum P,I,D parameter with regard to state of coupling. This method is simulated to Turbo-generating system with $2{\times}2$ multi input-output and made with electronic circuit, its response is very satisfactory.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 2001.12a
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• pp.177-180
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• 2001

The subject of this paper is to efficient Pm duty contort for two DC motor synchronousness in the system. Fuzzy controller have been successfully applied to many uncertain and complex industrial plant. So, It adapted fuzzy controller using compositional fuzzy rule so that change PH duty for speed control if the length of destination is different, And for unknow plant, it is the study to make the unknow transfer function system with fuzzy control method. This controller has been successfully applied to Pm duty control for the system synchronousness.

• Journal of Electrical Engineering and Technology
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• v.10 no.1
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• pp.379-387
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• 2015

A custom power device provides an integrated solution to the present problems that are faced by the utilities and power distribution. In this paper, a new controller is designed which is connected to a multiconverter unified power quality conditioner (MC-UPQC) for improving the power quality issues adopted modified synchronous reference frame (MSRF) theory with Fuzzy logic control (FLC) technique. This newly designed controller is connected to a source in order to compensate voltage and current in two feeders. The expanded concept of UPQC is multi converter-UPQC; this system has a two-series voltage source inverter and one shunt voltage source inverter connected back to back. This configuration will helps mitigate any type of voltage / current fluctuations and power factor correction in power distribution network to improve power quality issues. In the proposed system the power can be conveyed from one feeder to another in order to mitigate the voltage sag, swell, interruption and transient response of the system. The control strategies of multi converter- UPQC are designed based on the modified synchronous reference frame theory with fuzzy logic controller. The fast dynamics response of dc link capacitor is achieved with the help of Fuzzy logic controller. Different types of fault conditions are taken and simulated for the analysis and the results are compared with the conventional method. The relevant simulation and compensation performance analysis of the proposed multi converter-UPQC with fuzzy logic controller is performed.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.12 no.4
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• pp.1786-1795
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• 2011

The trajectory control for omni-directional mobile robot is not easy. Especially, the tracking control which system uncertainty problem is included is much more difficult. This paper develops trajectory controller of 3-wheels omni-directional mobile robot using fuzzy multi-layered algorithm. The fuzzy control method is able to solve the problems of classical adaptive controller and conventional fuzzy adaptive controllers. It explains the architecture of a fuzzy adaptive controller using the robust property of a fuzzy controller. The basic idea of new adaptive control scheme is that an adaptive controller can be constructed with parallel combination of robust controllers. This new adaptive controller uses a fuzzy multi-layered architecture which has several independent fuzzy controllers in parallel, each with different robust stability area. Out of several independent fuzzy controllers, the most suited one is selected by a system identifier which observes variations in the controlled system parameter. This paper proposes a design procedure which can be carried out mathematically and systematically from the model of a controlled system; related mathematical theorems and their proofs are also given. Finally, the good performance of the developed mobile robot is confirmed through live tests of path control task.

• Journal of the Korean Institute of Intelligent Systems
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• v.13 no.3
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• pp.287-292
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• 2003

The ability of robot is being various and complex. The robot is utilizing distance, image data and voice data for sensing its circumstance. This paper suggests the 2-layer fuzzy control as the algorithm that control robot with various sensor information. In a obstacle avoidance, it utilizes many range finders and classifies them into 3parts(front, left, right). In 3 sub-controllers, the controller executes fuzzy conference. And then it executes combined control with a combination of outputs of 3 sub-controllers in the second step. The text compares the 2-layer fuzzy controller with the hierarchical fuzzy controller that has analogous structure. And the performance of the 2-layer fuzzy controller is confirmed by application this controller to robot following, simulation to each other and real experiment.