• Journal of Institute of Control, Robotics and Systems
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• v.10 no.11
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• pp.1071-1076
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• 2004

It is a fuzzy controller that it is the most used method in the control of non-linear system. The most important part in the fuzzy controller is a design of fuzzy rules. Many algorithm that design fuzzy rules have proposed. And attention to the evolutionary computation is increasing in the recent days. Among them, the co-evolutionary algorithm is used in the design of optimal fuzzy rule. This paper takes advantage of a schema co-evolutionary algorithm. In order to verify the efficiency of the schema co-evolutionary algorithm, a fuzzy controller for the mobile robot control is designed by the schema co-evolutionary algorithm and it is compared with other parasitic co-evolutionary algorithm such as a virus-evolutionary genetic algorithm and a co-evolutionary method of Handa.

• Proceedings of the KIEE Conference
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• 1999.07b
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• pp.519-521
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• 1999

An adaptive fuzzy controller is constructed from a set of fuzzy IF-THEN rules whose parameters are adjusted on-line according to some adaptation law for the purpose of controlling the plant to track a given trajectory. Adaptive fuzzy controller of this paper is designed based on the Lyapunov synthesis approach The adaptive fuzzy controller is designed through the following steps: first, construct an initial controller based on linguistic descriptions(in the form of fuzzy IF-THEN rules) about the unknown plant from human experts; then, develop an adaptation law to adjust the parameters of the fuzzy controller on-line, the adaptive fuzzy controllers are used to control the inverted pendulum to track a given trajectory.

• Proceedings of the KIEE Conference
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• 1993.11a
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• pp.142-144
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• 1993

In this study, a Self-learning Neural-Fuzzy Networks is presented, Because of the fuzzy controller property, the designing problems of fuzzy if-then rules, membership functions and inference methods are very complex task. Thus in this paper we proposed the Neural-Fuzzy Networks composed by Sugeno and Takagi's fuzzy inference method and learned by using temporal back propagation algorithm. The proposed method can refine automatically the fuzzy if-then rules without human expert's knowledges. The induction motor servo system is used to demonstrate the effectiveness of the proposed control scheme and the feasibility of the acquired fuzzy controller. All results are supported by simulation.

• 제어로봇시스템학회:학술대회논문집
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• 2002.10a
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• pp.110.1-110
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• 2002

We propose a novel neuro-fuzzy system based on an efficient clustering method. It is a very useful method that improves the performance of a fuzzy model with small number of fuzzy rules. The fuzzy clustering methods are studied in the wide range of fuzzy modeling. One of them, the grid partition method has problem of exponentially increasing number of rules when the dimension of input or number of membership function is linearly increased. On the other hand, the Expectation Maximization algorithm is an efficient estimation for unknown parameters of the Gaussian mixture model. Here it is noted that the parameters can be used for fuzzy clustering method. In a fuzzy modeling, it is desired that...

• Journal of the Korean Institute of Intelligent Systems
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• v.9 no.6
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• pp.577-582
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• 1999

Using a sole input variable simplifies the design process for the fuzzy logic controller(FLC). This is called single-input fuzzy logic controller(SFLC). However it is still deficient in the capability of adapting to the varying operating conditions. We here design a single-input adaptive fuzzy logic controller(AFLC) using a switching function of the sliding mode control. The AFLC can directly incorporate linguistic fuzzy control rules into the controller. Hence some parameters of the membership functions characterizing the linguistic terms of the fuzzy rules can be adjusted by an adaptive law. In the proposed AFLC center values of fuzzy sets are directly adjusted by a fuzzy logic system. We prove that 1) its closed-loop system is globally stable in the sense that all signals involved are bounded and 2)its tracking error converges to zero asymptotically. We perform computer simulation using a nonlinear plant.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 1995.10b
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• pp.153-158
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• 1995

In this paper, A new design method of fuzzy modeling is presented for the model identification of nonlinear complex systems. The proposed fuzzy GMDH modeling implements system structure and parameter identification using GMDH(Group Method of Data Handling) algorithm and linguistic fuzzy implication rules from input and output data of processes. In order to identify premise structure and parameter of fuzzy implication rules, GMDH algorithm and fuzzy reasoning method are used and the least square method is utilized for the identification of optimum consequence parameters. Time series data for gas furnaceare those for sewage treatment process are used for the purpose of evaluating the performance of the proposed fuzzy GMDH modeling. The results show that the proposed method can produce the fuzzy model with higher accuracy than other works achieved previously.

• 제어로봇시스템학회:학술대회논문집
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• 1992.10a
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• pp.7-12
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• 1992

This paper proposes a sensor-based path planning method which utilizes fuzzy logic and neural network for obstacle avoidance of a mobile robot in uncertain environments. In order to acquire the information about the environment around the mobile robot, the ultrasonic sensors mounted on the front of mobile robot are used. The neural network, whose inputs are preprocessed by ultrasonic sensor readings, informs the mobile robot of the situation of environment in which mobile robot is at the present instant. Then, according to the situation class, the fuzzy rules are fired to make a decision on the mobile robot action. In addition, this method can be implemented real time since the number of fuzzy rules used to avoid the obstacle is small. Fuzzy rules are constructed based on the human reasoning and tuned by iterative simulations. The effective of the proposed avoidance method is verified by a series of simulations.

• 제어로봇시스템학회:학술대회논문집
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• 1992.10b
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• pp.564-568
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• 1992

A neural optimization network is designed to solve the collsion-free inverse kinematics problem for redundant robot manipulators under the constraints of joint limits, maximum velocities and maximum accelerations. And the fuzzy rules are proposed to determine the weightings of neural optimization networks to avoid the collision between robot manipulator and obstacles. The inputs of fuzzy rules are the resultant distance, change of the distance and sum of the changes. And the output of fuzzy rules is defined as the capability of collision avoidance of joint differential motion. The weightings of neural optimization networks are adjusted according to the capability of collision avoidance of each joint. To show the validities of the proposed method computer simulation results are illustrated for the redundant robot with three degrees of freedom,

• 제어로봇시스템학회:학술대회논문집
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• 1990.10b
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• pp.1371-1376
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• 1990

The paper presents a method of constructing simple fuzzy control rules for the determination of stabilizing signals of automatic voltage regulator and governor, which are controllers of electric power systems. Fuzzy control rules are simplified by considering a coordinate transformation with the rotation angle .theta. on the phase plane, and by expanding the range of membership functions. Also, two rotation angles .theta. $_{1}$ and .theta. $_{2}$ are selected for the linearizable region and the nonlinear one of the system, respectively. Here, .theta. $_{1}$ is chosen by the pole assignment method, and .theta. $_{2}$ by a performance index. Fuzzy inference is applied to the connection of two rotation angles .theta. $_{1}$ and .theta. $_{1}$ by regarding the distance from the desired equilibrium point as a variable of condition parts. The control effect is demonstrated by an application of the proposed method to one-machine infinite-bus power system.

• Proceedings of the KIEE Conference
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• 2002.07d
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• pp.2196-2198
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• 2002

Fuzzy controller design consists of intuition, and any other information about how to control system, into a set of rules. These rules can then be applied to the system. It is very important to decide parameters of IF-THEN rules. Because Fuzzy controller can make more adequate force to the plant by means of parameter optimization, which is accomplished by learning procedure. In this paper, we apply adaptive fuzzy controller designed to the Rotary Inverted Pendulum.