• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 2003.09a
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• pp.388-391
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• 2003

We've been following research on the obstacle avoidance that is based on fuzzy control. We previously proposed a new method of automatically generating membership functions, which play an important role in improving accuracy of fuzzy control, by using genetic programming (GP). In this paper, we made two improvements to our proposed method, for the purpose of achieving better intelligence in fuzzy robots. First, the mutation rate is made to change dynamically, according to the coupled chaotic system. Secondly, the population partitioning using deme is introduced by parallel processing. The effectiveness of these improvements is demonstrated through several computer simulations.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 1995.10b
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• pp.40-44
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• 1995

In this paper, we propose a retrieval system using knowledges of database expressed linguistically, where the relation between data are constructed by FCM. Several algorithms have been proposed to solve the major problem in the conventional retrieval system that the system doesn't reply in case of no data equal to user's query, and to express knowledge of database linguistically. This paper proposes the improved method of adding new cluster and the method of retrieving database from user's query. The validity of this retrieval system is shown by applying its algorithm to an example : the mail order service in post office.

• 제어로봇시스템학회:학술대회논문집
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• 2003.10a
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• pp.1593-1597
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• 2003

The home service robot supports human beings by performing various kinds of works at home. This paper presents a simple control method for opening a door from the viewpoint of the mobile manipulation. The simulation shows various results of path planning and motion planning for opening a door. The joint trajectories were generated by the simulation system. In general, a six-axis force/torque sensor at an end-effector is needed in order to maintain the static equilibrium of the manipulator. But we show another method. From three components of applied forces which was directly obtained by the three-axis force sensor and three components of applied forces which was indirectly estimated by the joint-torque sensors, all of joint torques that will exactly balance forces at the end-effector in the static situation can be found. It is more practical method than using a six-axis force sensor in a wrist. Experimental results have shown that the opening a door can be realized more effectively from the suggested control method of mobile manipulation.

• Journal of the Korean Institute of Intelligent Systems
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• v.13 no.1
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• pp.24-30
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• 2003

In this paper, we propose a novel predictive control method, which uses a wavelet neural network as a predictor, for the control of chaotic systems. In our method, we use the gradient descent method for training the parameter of a wavelet neural network. The control signals are directly obtained by minimizing the difference between a reference signal and the output of a wavelet neural network. To verify the efficiency of our method, we apply it to the Doffing and the Henon system, which are a representative continuous and discrete time chaotic system respectively, and compare with the results of generalized predictive control using multi-layer perceptron.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 2003.09b
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• pp.129-132
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• 2003

This paper proposes a novel and efficient intelligent digital redesign technique for a Takagi-Sugeno (TS) fuzzy system. The term of intelligent digital redesign involves converting an existing analog fuzzy-model-based controller into an equivalent digital counterpart in the sense of state matching. In this paper, we suggest the discretization method based on the dual-rate sampling approximation is first proposed, and then attempt to globally match the states of the overall closed-loop TS fuzzy system with the pre-designed analog fuzzy-model-based controller and those with the digitally redesigned fuzzy-model-based controller. To show the feasibility and the effectiveness of the proposed method, a computer simulation is provided.

• Earthquakes and Structures
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• v.24 no.1
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• pp.1-9
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• 2023

A model for calculating structure interacted mechanics is proposed. A structural interaction model and controller design based on tuned mass damping (TMD) was developed to control the induced vibration. A key point is to introduce a new analytical model to evaluate the properties of the TMD that recognizes the motion-dependent nonlinear response observed in the simulations. Aiming at the problem of increased current harmonics and low efficiency of permanent magnet synchronous motors for electric vehicles due to dead time effect, a dead time compensation method based on neural network filter and current polarity detection is proposed. Firstly, the DC components and the higher harmonic components of the motor currents are obtained by virtue of what the neural network filters and the extracted harmonic currents are adjusted to the required compensation voltages by virtue of what the neural network filters. Then, the extracted DC components are used for current polarity dead time compensation control to avert the false compensation when currents approach zero. The neural network filter method extracts the required compensation voltages from the speed component and the current polarity detection compensation method obtains the required compensation voltages by discriminating the current polarity. The combination of the two methods can more precisely compensate the dead time effect of the control system to improve the control performance. Furthermore, based on the relaxed method, the intelligent approach of stability criterion can be regulated appropriately and the artificial TMD was found to be effective in reducing cross-wind vibrations.

• International Journal of Control, Automation, and Systems
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• v.1 no.1
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• pp.93-100
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• 2003

Accuracy in maneuvering target tracking using multiple models relies upon the suit-ability of each target motion model to be used. To construct multiple models, the interacting multiple model (IMM) algorithm and the adaptive IMM (AIMM) algorithm require predefined sub-models and predetermined acceleration intervals, respectively, in consideration of the properties of maneuvers. To solve these problems, this paper proposes the GA-based IMM method as an intelligent tracking method for a maneuvering target. In the proposed method, the acceleration input is regarded as an additive process noise, a sub-model is represented as a fuzzy system to compute the time-varying variance of the overall process noise, and, to optimize the employed fuzzy system, the genetic algorithm (GA) is utilized. The simulation results show that the proposed method has a better tracking performance than the AIMM algorithm.

• Journal of the Korean Institute of Intelligent Systems
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• v.14 no.6
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• pp.776-782
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• 2004

Generally, Max-Min CRI (Compositional Rule of Inference ) method by Zadeh and Mamdani is used in the conventional fuzzy inference. However, owing to the problems of Max-Min CRI method, the inference often results in significant error regions specifying the difference between the desired outputs and the inferred outputs. In this paper, I propose a New Max-Min CRI method which can solve some problems of the conventional Max-Min CRI method. And then this method is simulated in a D.C.series motor, which is a bench marking system in control systems, and showed that the new method performs better than the other fuzzy inference methods.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 1998.06a
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• pp.646-651
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• 1998

Fuzzy control has been used successfully in many practical applications. In traditional methods, experience and control knowledge of human experts are needed to design fuzzy controllers. However, it takes much time and cost. In this paper, an automatic design method for fuzzy controllers using genetic algorithms is proposed. In the method, we proposed an effective encoding scheme and new genetic operators. The maximum number of linguistic terms is restricted to reduce the number of combinatorial fuzzy rules in the research space. The proposed genetic operators maintain the correspondency between membership functions and control rules. The proposed method is applied to a cart centering problem. The result of the experiment has been satisfactory compared with other design methods using genetic algorithms.

• 제어로봇시스템학회:학술대회논문집
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• 2000.10a
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• pp.182-182
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• 2000

This paper proposes a self tuning fuzzy inference method by the genetic algorithm in the fuzzy-sliding mode control for a robot. Using this method, the number of inference rules and the shape of membership functions are optimized without an expert in robotics. The fuzzy outputs of the consequent part are updated by the gradient descent method. And, it is guaranteed that the selected solution become the global optimal solution by optimizing the Akaike's information criterion. The trajectory trucking experiment of the polishing robot system shows that the optimal fuzzy inference rules are automatically selected by the genetic algorithm and the proposed fuzzy-sliding model controller provides reliable tracking performance during the polishing process.