• Journal of the Korean Institute of Intelligent Systems
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• v.21 no.4
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• pp.499-505
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• 2011

In this paper, we proposed an adaptive fuzzy sliding mode control for nonlinear systems without parameter projection method. By modifying the controller structure, the parameters of the estimated input gain function are guaranteed not being identically zero and it is shown that the control scheme will not cause any implementation problem even if the estimated value of input gain function is zero at any moment during on-line operations. Except for the input gain function which an approximate estimate for its lower bound is needed, the proposed control scheme does not assume a priori the exact values of the bounding parameters. Based on Lyapunov synthesis methods, the overall control system guarantees that the tracking error asymptotically converges to zero and that all signals involved in controller are uniformly bounded. This can be illustrated by the simulation results for an inverted pendulum system.

• Journal of the Korean Institute of Intelligent Systems
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• v.19 no.3
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• pp.337-342
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• 2009

This paper concerns delay-range-dependent robust stability and stabilization for time-delay nonliner system via T-S fuzzy model approach. The time delay is assumed to be a time-varying continuous function belonging to a given range. On the basis of a novel Lyapunov-Krasovskii functional, which includes the information of the range, delay-range-dependent stability criteria are established in terms of linear matrix inequality. It is shown that the new criteria can provide less conservative results than some existing ones. Moreover, the stability criteria are also used to design the stabilizing state-feedback controllers. Numerical examples are given to demonstrate the applicability of the proposed approach.

• Journal of the Institute of Convergence Signal Processing
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• v.19 no.2
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• pp.77-88
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• 2018

This paper proposes a RBFNN(Radial Basis Function Neural Network) based decentralized adaptive tracking control scheme using PSO(Particle Swarm Optimization) for an uncertain electrically driven robot system with input saturation. Practically, the magnitudes of input voltage and current signals are limited due to the saturation of actuators in robot systems. The proposed controller overcomes this input saturation and does not require any robot link and actuator model parameters. The fitness function used in the presented PSO scheme is expressed as a multi-objective function including the magnitudes of voltages and currents as well as the tracking errors. Using a PSO scheme, the control gains and the number of the RBFs are tuned automatically and thus the performance of the control system is improved. The stability of the total control system is guaranteed by the Lyapunov stability analysis. The validity and robustness of the proposed control scheme are verified through simulation results.

• Journal of Auto-vehicle Safety Association
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• v.5 no.2
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• pp.24-31
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• 2013

This paper describes a coordinated control algorithm for rear-side collision avoidance. In order to assist driver actively and increase driver's safety, the proposed coordinated control algorithm is designed to combine lateral control using a steering torque overlay by Motor Driven Power Steering (MDPS) and differential braking by Vehicle Stability Control (VSC). The main objective of a combined control strategy is twofold. The one is to prevent the collision between the subject vehicle and approaching vehicle in the adjacent lanes. The other is to limit actuator's control inputs and vehicle dynamics to safe values for the assurance of the driver's comfort. In order to achieve these goals, the Lyapunov theory and LMI optimization methods has been employed. The proposed coordinated control algorithm for rear-side collision avoidance has been evaluated via simulation using CarSim and MATLAB/Simulink.

• Journal of the Korean Institute of Intelligent Systems
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• v.22 no.2
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• pp.225-231
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• 2012

In this paper, we propose a method for designing the path tracking controller using an approach angle concept for an underactuated autonomous underwater vehicle (AUV). The AUV is controlled by the surge speed and yaw rate: there is no side thruster. To solve this underactuated AUV problem in the path tracking, we introduce an approach angle concept which makes the AUV converge to the reference path. And we design the path tracking controller using the proposed approach angle. To design the path tracking controller, we obtain the new vehicle's error dynamics in the body-fixed frame, and then design the path tracking controller based on Lypunov direct method. Finally, some simulation results demonstrate the effectiveness of the proposed controller.

• Journal of Acupuncture Research
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• v.18 no.2
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• pp.67-78
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• 2001

The aim of this study was to examine the effects of low frequency electroacupuncture(EA) at the $B_{62}$ (Shinmaek) $K_6$(Chohae) on vascular dementia in humans using nonlinear dynamics. Electroencephalogram(EEG) is a multi-scaled signal consisting of several components of time series with different dominant frequency ranges and different origins. Nonlinear measures of the EEG like the correlation dimension ($D_2$) and the first positive Lyapunov exponent ($L_1$) reflect the complexity of the EEG. In this study, $D_2$ was used as a measure of complexity. Sixteen channel EEG study was carried out in six subjects (5 females and 1 males; $age=83.83{\pm}7.19years$). We found that the baseline $D_2$ values of the EEG at F4 and F8 channels (P<0.01) were lowered than during the acupuncture treatment, indicating decreased complexity of the EEG. However, the comparison with that before and after the treatment shows no significant differences in all channels.

• Journal of the Korean Institute of Intelligent Systems
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• v.23 no.2
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• pp.151-157
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• 2013

This paper proposes an online adaptive fuzzy controller for a wind energy conversion system (WECS) that is intrinsically highly nonlinear plant. In real application, to obtain exact system parameters such as power coefficient, many measuring instruments and off-line implementations are required, which is very difficult to perform. This shortcoming can be avoided by introducing fuzzy system in the controller design in this paper. The proposed adaptive fuzzy control scheme using self-structuring algorithm requires no system parameters to meet control objectives. Even the structure of the fuzzy system is automatically grows on-line, which distinguishes our proposed algorithm over the previously proposed fuzzy control schemes. Combining derivative estimator for wind velocity, the whole closed-loop system is shown to be stable in the sense of Lyapunov.

• Journal of Intelligence and Information Systems
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• v.4 no.1
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• pp.133-147
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• 1998

Recently, many researchers have been involved in finding deterministic equations which can accurately predict future event, based on chaotic theory, or fractal theory. The theory says that some events which seem very random but internally deterministic can be accurately predicted by fractal equations. In contrast to the conventional methods, such as AR model, MA, model, or ARIMA model, the fractal equation attempts to discover a deterministic order inherent in time series data set. In discovering deterministic order, researchers have found that neural networks are much more effective than the conventional statistical models. Even though prediction accuracy of the network can be different depending on the topological structure and modification of the algorithms, many researchers asserted that the neural network systems outperforms other systems, because of non-linear behaviour of the network models, mechanisms of massive parallel processing, generalization capability based on adaptive learning. However, recent survey shows that prediction accuracy of the forecasting models can be determined by the model structure and data structures. In the experiments based on actual economic data sets, it was found that the prediction accuracy of the neural network model is similar to the performance level of the conventional forecasting model. Especially, for the data set which is deterministically chaotic, the AR model, a conventional statistical model, was not significantly different from the MLP model, a neural network model. This result shows that the forecasting model. This result shows that the forecasting model a, pp.opriate to a prediction task should be selected based on characteristics of the time series data set. Analysis of the characteristics of the data set was performed by fractal analysis, measurement of Hurst index, and measurement of Lyapunov exponents. As a conclusion, a significant difference was not found in forecasting future events for the time series data which is deterministically chaotic, between a conventional forecasting model and a typical neural network model.

• Journal of Korean Port Research
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• v.15 no.1
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• pp.37-46
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• 2001

Fuzzy logic control(FLC) is composed of three parts : fuzzy rule-bases, membership functions, and scaling factors. Well-defined fuzzy rule-base should contain proper physical intuition on the plant, so are needed lots of experiences of the skillful expert. When membership functions are considered, some parameters on the memberships function such as function shape, support, allocation density should be selected well. The rule of scaling factors is 'scaling'(amplifying or reducing) for both input and output signals of the FLC to fit in the membership function support and to operate the plant intentionally. To get a better performance of the FLC, it is necessary to adjust the parameters of the FLC. In general, the adaptation of the scaling factors is the most effective adjustment scheme, compared with that of the fuzzy rule-base or membership function parameters. This study proposes the adaptation scheme of the scaling factors. When the adaptation is performed on-line, the stability of the adaptive FLC should be guaranteed. The stable FLC system can be designed with stability analysis in the sense of Lyapunov stability. To adapt the scaling factors for the error signals, the concept of the conventional MRAC would be introduced into slightly modified form. A tracking accuracy of the control system would be enhanced by the modified shape and support of the membership function. The simulation is achieved on the pilot plant with the hydraulic steering control of a UCT(Unmanned Container Transporter) of which modeling dynamics have lots of severe uncertainties and modeling errors.

• Structural Engineering and Mechanics
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• v.24 no.3
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• pp.375-393
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• 2006

Under high velocity, pulse type near source earthquakes semi-active control systems are very effective in reducing seismic response base isolated structures. Semi-active control systems can be classified as: 1) independently variable stiffness, 2) independently variable damping, and 3) combined variable stiffness and damping systems. Several researchers have studied the effectiveness of independently varying damping systems for seismic response reduction of base isolated structures. In this study effectiveness of a combined system consisting of a semi-active independently variable stiffness (SAIVS) device and a magnetorheological (MR) damper in reducing seismic response of base isolated structures is analytically investigated. The SAIVS device can vary the stiffness, and hence the period, of the isolation system; whereas, the MR damper enhances the energy dissipation characteristics of the isolation system. Two separate control algorithms, i.e., a nonlinear tangential stiffness moving average control algorithm for smooth switching of the SAIVS device and a Lyapunov based control algorithm for damping variation of MR damper, are developed. Single and multi degree of freedom systems consisting of sliding base isolation system and both the SAIVS device and MR damper are considered. Results are presented in the form of nonlinear response spectra, and effectiveness of combined variable stiffness and variable damping system in reducing seismic response of sliding base isolated structures is evaluated. It is shown that the combined variable stiffness and variable damping system leads to significant response reduction over cases with variable stiffness or variable damping systems acting independently, over a broad period range.