• Journal of Power Electronics
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• v.13 no.3
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• pp.369-380
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• 2013

Direct torque control based on space vector modulation (SVM-DTC) protects the DTC transient merits. Furthermore, it creates better quality steady-state performance in a wide speed range. The modified method of DTC using SVM improves the electrical magnitudes of asynchronous machines, such as minimizing the stator current distortions, the stator flux with electromagnetic torque without ripple, the fast response of the rotor speed, and the constant switching frequency. In this paper, the proposed method is based on two new control strategies for direct torque control with space vector modulation. First, fuzzy logic control is used instead of the PI torque and a PI flux controller to minimizing the torque error and to achieve a constant switching frequency. The voltages in the direct and quadratic reference frame ($V_d$, $V_q$) are achieved by fuzzy logic control. In this scheme, the switching capability of the inverter is fully utilized, which improves the system performance. Second, the close loop of stator flux estimation based on the voltage model and a low pass filter is used to counteract the drawbacks in the open loop of the stator flux such as the problems saturation and dc drift. The response of this new control strategy is compared with DTC-SVM. The experimental and simulation results demonstrate that the proposed control topology outperforms the conventional DTC-SVM in terms of system robustness and eliminating the bad outcome of dc-offset.

• Journal of the Korean Institute of Intelligent Systems
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• v.9 no.4
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• pp.420-425
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• 1999

The maximum demand controller is an electrical equipment installed at the consumer side of power system for monitoring the electrical energy consumed during every integrating period and preventing the target maximum demand (MD) being exceeded by disconnecting sheddable loads. By avoiding the peak loads and spreading the energy requirement the controller contributes to maximizing the utility factor of the generator systems. It results in not only saving the energy but also reducing the budget for constructing the natural base facilities by keeping thc number of generating plants ~ninimumT. he conventional MD controllers often bring about the large number of control actions during the every inteyating period and/or undesirable loaddisconnecting operations during the beginning stage of the integrating period. These make the users aviod the MD controllers. In this paper. fuzzy control technique is used to get around the disadvantages of the conventional MD control system. The proposed MD controller consists of the predictor module and the fuzzy MD control module. The proposed forecasting method uses the SOFM neural network model, differently from time series analysis, and thus it has inherent advantages of neural network such as parallel processing, generalization and robustness. The MD fuzzy controller determines the sensitivity of control action based on the time closed to the end of the integrating period and the urgency of the load interrupting action along the predicted demand reaching the target. The experimental results show that the proposed method has more accurate forecastinglcontrol performance than the previous methods.

• Journal of the Korean Society of Propulsion Engineers
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• v.20 no.2
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• pp.56-66
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• 2016

Robustness is essential for model based FDI (Fault Detection and Isolation) and it is inevitable to have modeling errors and sensor signal noises during the process of FDI. This study suggests an improved method by applying NARX (Nonlinear Auto Regressive eXogenous) model and Kalman estimator in order to cope with problems caused by linear model errors and sensor signal noises in the process of fault diagnoses. Fault decision is made by the probability of the trend of gradually accumulated errors applying Fuzzy logic, which are robust to instantaneous sensor signal noises. Reliability of fault diagnosis is verified under various fault simulations.

• Journal of the Korean Institute of Intelligent Systems
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• v.9 no.6
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• pp.598-604
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• 1999

A new fuzzy control algorithm for the control of active magnetic bearing (AMB) systems is proposed in th~sp aper. It combines PDC algorithm based on the LMI design of Joh et al. [4,5] and Mamdani-type control rules using fuzzy singletons to handle the nonlinear characteristics of AMB systems efficiently. They are named fine mode control and coarse mode control, respectively. The coarse mode control yields fast response for large deviation of the rotor and the fine mode control gives desired transient response for small deviation of the rotor. The proposed algorithm is applied to an AMB system to verify the performance of the proposed method. The comparison of the proposed method with a linear controller using a linearized model about the equilibrium point and the PDC algorithm show the superiority of the proposed algorithm.

• Journal of the Korean Institute of Intelligent Systems
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• v.5 no.1
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• pp.52-64
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• 1995

Robot manipulator is a highly nonlinear-time varying system. Therefore, a lot of control theory has been applied to the system. Robot manipulator has two types of control; one is path planning, another is path tracking. In this paper, we select the path tracking, and for this purpose, propose the intelligent control¬ler which is combined with fuzzy logic and neural network. The fuzzy logic provides an inference morphorlogy that enables approximate human reasoning to apply to knowledge-based systems, and also provides a mathematical strength to capture the uncertainties associated with human cognitive processes like thinking and reasoning. Based on this fuzzy logic, the fuzzy logic controller(FLC) provides a means of converhng a linguistic control strategy based on expert knowledge into automahc control strategy. But the construction of rule-base for a nonlinear hme-varying system such as robot, becomes much more com¬plicated because of model uncertainty and parameter variations. To cope with these problems, a auto-tuning method of the fuzzy rule-base is required. In this paper, the GA-based Fuzzy-Neural control system combining Fuzzy-Neural control theory with the genetic algorithm(GA), that is known to be very effective in the optimization problem, will be proposed. The effectiveness of the proposed control system will be demonstrated by computer simulations using a two degree of freedom robot manipulator.

• International Journal of Ocean System Engineering
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• v.1 no.4
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• pp.192-197
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• 2011

This paper describes the mathematical modeling, control algorithm, system design, hardware implementation and experimental test of a Manta-type Unmanned Underwater Vehicle (MUUV). The vehicle has one thruster for longitudinal propulsion, one rudder for heading angle control and two elevators for depth control. It is equipped with a pressure sensor for measuring water depth and Doppler Velocity Log for measuring position and angle. The vehicle is controlled by an on-board PC, which runs with the Windows XP operating system. The dynamic model of 6DOF is derived including the hydrodynamic forces and moments acting on the vehicle, while the hydrodynamic coefficients related to the forces and moments are obtained from experiments or estimated numerically. We also utilized the values obtained from PMM (Planar Motion Mechanism) tests found in the previous publications for numerical simulations. Various controllers such as PID, Sliding mode, Fuzzy and $H{\infty}$ are designed for depth and heading angle control in order to compare the performance of each controller based on simulation. In addition, experimental tests are carried out in a towing tank for depth keeping and heading angle tracking.

• Journal of the Korean Institute of Intelligent Systems
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• v.16 no.2
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• pp.138-143
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• 2006

This paper presents intelligent digital redesign method for hybrid state space fuzzy-model-based controllers. For effectiveness and stabilization of continuous-time uncertain nonlinear systems under discrete-time controller, Takagi-Sugeno(TS) fuzzy model is used to represent the complex system. And global approach design problems viewed as a convex optimization problem that we minimize the error of the norm bounds between nonlinearly interpolated linear operators to be matched. Also, by using the bilinear and inverse bilinear approximation method, we analyzed nonlinear system's uncertain parts more precisely. When a sampling period is sufficiently small, the conversion of a continuous-time structured uncertain nonlinear system to an equivalent discrete-time system have proper reason. Sufficiently conditions for the global state-matching of the digitally controlled system are formulated in terms of linear matrix inequalities (LMIs). Finally, a TS fuzzy model for the chaotic Lorentz system is used as an . example to guarantee the stability and effectiveness of the proposed method.

• Proceedings of the Korea Committee for Ocean Resources and Engineering Conference
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• 2000.10a
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• pp.79-83
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• 2000

A sliding mode fuzzy control (SMFC) with disturbance estimator is applied to design a controller for the third generation benchmark problem on an wind-excited building. A distinctive feature in vibration control of large civil infrastructure is the existence of large disturbances, such as wind, earthquake, and sea wave forces. Those disturbances govern the behavior of the structure, however, they cannot be precisely measured, especially for the case of wind-induced vibration control. Since the structural accelerations are measured only at a limited number of locations without the measurement of the wind forces, the structure of the conventional control may have the feed-back loop only. General structure of the SMFC is composed of a compensation part and a convergent part. The compensation part prevents the system diverge, and the convergent part makes the system converge to the sliding surface. The compensation part uses not only the structural response measurement but also the disturbance measurement, so the SMFC has a feed-back loop and a feed-forward loop. To realize the virtual feed-forward loop for the wind-induced vibration control, disturbance estimation filter is introduced. the structure of the filter is constructed based on an auto regressive model for the stochastic wind force. This filter estimates the wind force at each time instance based on the measured structural responses and the stochastic information of the wind force. For the verification of the proposed algorithm, a numerical simulation is carried out on the benchmark problem of a wind-excited building. The results indicate that the present control algorithm is very efficient for reducing the wind-induced vibration and that the performance indices improve as the filter for wind force estimation is employed.

• International Journal of Fuzzy Logic and Intelligent Systems
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• v.16 no.4
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• pp.281-292
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• 2016

The proposed scheme is based on the modified perturb and observe (P&O) algorithm combined with the sliding mode technique. A modified P&O algorithm based sliding mode controller is developed to study the effects of partial shade, temperature, and insolation on the performance of maximum power point tracking (MPPT) used in photovoltaic (PV) systems. Under partially shaded conditions and temperature, the energy conversion efficiency of a PV array is very low, leading to significant power losses. Consequently, increasing efficiency by means of MPPT is particularly important. Conventional techniques are easy to implement but produce oscillations at MPP. The proposed method is applied to a model to simulate the performance of the PV system for solar energy usage, which is compared to the conventional methods under non-uniform insolation improving the PV system utilization efficiency and allowing optimization of the system performance. The modified perturb and observe sliding mode controller successfully overcomes the issues presented by non-uniform conditions and tracks the global MPP. Compared to MPPT techniques, the proposed technique is more efficient; it produces less oscillation at MPP in the steady state, and provides more precise tracking.

• 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.