• Journal of the Korean Society for Precision Engineering
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• v.26 no.12
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• pp.69-77
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• 2009

In this study an ANFIS-based trajectory tracking motion control algorithm is proposed for autonomous garage and parallel parking of a model car. The ANFIS controller is trained off-line using data set which obtained by Mandani fuzzy inference system and thereby the processing time decreases almost in half. The controller with a steering delay compensator is tuned through simulations performed under MATLAB/Simulink environment. Experiments are carried out with the model car for garage and parallel parking. The experimental results show that the trajectory tracking performance is satisfactory under various initial and road conditions

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 1998.10a
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• pp.447-452
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• 1998

A fault is considered as a variation of physical parameters; therefore the design of fault detection and identification(FDI) can be reduced to the parameter identification of a non linear system and to the association of the set of the estimated parameters with the mode of faults. ANFIS(Adaptive Neuro-Fuzzy Inference System) which contains multiple linear models as consequent part is used to model non linear systems. In this paper, we proposes an FDI system for non linear systems using ANFIS. The proposed diagnositc system consists of two ANFISs which operate in two different modes (parallel-and series-parallel mode). It generates the parameter residuals associated with each modes of faults which can be further processed by additional RBF (Radial Basis function) network to identify the faults. The proposed FDI scheme has been tested by simultation on a two-tank system

• Journal of information and communication convergence engineering
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• v.10 no.2
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• pp.149-155
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• 2012

This paper presents mobile robot control architecture of hierarchical behaviors, inspired by biological life. The system is reactive, highly parallel, and does not rely on representation of the environment. The behaviors of the system are designed hierarchically from the bottom-up with priority given to primitive behaviors to ensure the survivability of the robot and provide robustness to failures in higher-level behaviors. Fuzzy logic is used to perform command fusion on each behavior's output. Simulations of the proposed methodology are shown and discussed. The simulation results indicate that complex tasks can be performed by a combination of a few simple behaviors and a set of fuzzy inference rules.

• Smart Structures and Systems
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• v.25 no.4
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• pp.401-408
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• 2020

This paper deals with the problem of global stabilization for a class of nonlinear control systems. An effective approach is proposed for controlling the system interaction of structures through a combination of parallel distributed compensation (PDC) intelligent controllers and fuzzy observers. An efficient approximate inference algorithm using expectation propagation and a Bayesian additive model is developed which allows us to predict the total number of control systems, thereby contributing to a more adaptive trajectory for the closed-loop system and that of its corresponding model. The closed-loop fuzzy system can be made as close as desired, so that the behavior of the closed-loop system can be rigorously predicted by establishing that of the closed-loop fuzzy system.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.67 no.1
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• pp.96-102
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• 2018

For efficient operating strategy of electric power system, forecasting of daily peak electric load is an important but difficult problem. Therefore a daily peak electric load forecasting system using a neural network and fuzzy system is presented in this paper. First, original peak load data is interpolated in order to overcome the shortage of data for effective prediction. Next, the prediction of peak load using these interpolated data as input is performed in parallel by a neural network predictor and a fuzzy predictor. The neural network predictor shows better performance at drastic change of peak load, while the fuzzy predictor yields better prediction results in gradual changes. Finally, the superior one of two predictors is selected by the rules based on rough sets at every prediction time. To verify the effectiveness of the proposed method, the computer simulation is performed on peak load data in 2015 provided by KPX.

• 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 Advanced Marine Engineering and Technology
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• v.39 no.5
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• pp.563-569
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• 2015

A chemical reaction occurring in CSTR (Continuous Stirred Tank Reactor) is significantly affected by the concentration, temperature, pressure, and reacting time of materials, and thus it has strong nonlinear and time-varying characteristics. Also, when an existing linear PID controller with fixed gain is used, the performance could deteriorate or could be unstable if the system parameters change due to the change in the operating point of CSTR. In this study, a technique for the design of a fuzzy PD plus I controller was proposed for the temperature control of a CSTR process. In the fuzzy PD plus I controller, a linear integral controller was added to a fuzzy PD controller in parallel, and the steady-state performance could be improved based on this. For the fuzzy membership function, a Gaussian type was used; for the fuzzy inference, the Max-Min method of Mamdani was used; and for the defuzzification, the center of gravity method was used. In addition, the saturation state of the actuator was also considered during controller design. The validity of the proposed method was examined by comparing the set-point tracking performance and the robustness to the parameter change with those of an adaptive controller and a nonlinear proportional-integral-differential controller.

• Journal of the Korean Institute of Intelligent Systems
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• v.5 no.4
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• pp.77-86
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• 1995

The new architecture that fuzzy logic control(FLC) with difficulties for tuning membership function (MF) is parallel with neural networks(NN) to be learned from the output of FLC is proposed. Therefore proposed scheme has the characteristics to utilize the expert knowledge in design process, to be learned during the operation without any learning mode. In this architecture, the function of the FLC is to supply the sliding surface which is constructed on the phase plane by rule base for giving the desired control characteristics and learning criterion of NN and the stabilization of the control performance before NN is learned, The function of the NN is to let the system trajectory be tracked to the sliding surface and reached to the stable point.

• Journal of the Korea Society of Computer and Information
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• v.24 no.11
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• pp.51-59
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• 2019

Due to the exponential growth of access information on the web, the need for predicting web users' next access has increased. Various models such as markov models, deep neural networks, support vector machines, and fuzzy inference models were proposed to handle web access prediction. For deep learning based on neural network models, training time on large-scale web usage data is very huge. To address this problem, deep neural network models are trained on cluster of computers in parallel. In this paper, we investigated impact of several important spark parameters related to data partitions, shuffling, compression, and locality (basic spark parameters) for training Multi-Layer Perceptron model on Spark standalone cluster. Then based on the investigation, we tuned basic spark parameters for training Multi-Layer Perceptron model and used it for tuning Spark when training Multi-Layer Perceptron model for web access prediction. Through experiments, we showed the accuracy of web access prediction based on our proposed web access prediction model. In addition, we also showed performance improvement in training time based on our spark basic parameters tuning for training Multi-Layer Perceptron model over default spark parameters configuration.