• Proceedings of the KIEE Conference
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• 2009.07a
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• pp.778_779
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• 2009

The maximum output torque developed by the machine is dependent on the allowable current rating and maximum voltage that the inverter can supply to the machine. Therefore, to use the inverter capacity fully, it is desirable to use the control scheme considering the voltage and current limit condition, which can yield the maximum torque per ampere over the entire speed range. The paper is proposed maximum torque control of induction motor drive using adaptive learning neuro fuzzy controller and artificial neural network(ANN). The control method is applicable over the entire speed range and considered the limits of the inverter's current and voltage rated value. For each control mode, a condition that determines the optimal d, q axis current $_i_{ds}$, $i_{qs}$ for maximum torque operation is derived. The proposed control algorithm is applied to induction motor drive system controlled adaptive learning neuro fuzzy controller and ANN controller, the operating characteristics controlled by maximum torque control are examined in detail. Also, this paper is proposed the analysis results to verify the effectiveness of the adaptive learning neuro fuzzy controller and ANN controller.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.60 no.10
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• pp.1924-1934
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• 2011

This paper is concerned with the robustness evaluations of the guidance controller for a bimodal tram which is being developed by the Korea Railroad Research Institute (KRRI). The bimodal tram is an all-wheel steered multiple-articulated vehicle as a new kind of transportation vehicle. This vehicle has to be equipped with an automatic guidance system. In [1], such a controller has been recently proposed. However, since the performance is affected by weight change of the vehicle due to number of the passenger, model parameter uncertainties depending on the state of friction and the elasticity of the tire, and a typhoon, the controller designed must be examined with these conditions. As expected, because the vehicle dynamics is highly nonlinear, for the sake of investigating the robustness of the controller we compose two simulation ways based on the vehicle models which are implemented by the ADAMS and the MATLAB/LabVIEW toolboxes. Different uncertainties and a typhoon disturbance have been considered for the simulation conditions. Simulation results are shown.

• Journal of Electrical Engineering and Technology
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• v.10 no.2
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• pp.688-698
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• 2015

This paper presents a modeling and a controller design for a hybrid wind turbine generator, especially with an operating mode of battery energy-storage system and a dumpload that contribute to the frequency control of the system while diesel-synchronous unit is not in operation. The proposed control scheme is based on a robust tracking controller, which takes an account of system uncertainties due to the wind flow and load variations. In order to provide robustness for system uncertainties, the range of operation is partitioned into three operating conditions as sub-models in the controller design. In the simulation study, the proposed robust tracking controller (RTC) is compared with the conventional proportional-integral (PI) controller. Simulation results show that the effectiveness of the RTC against disturbances caused by wind speed and load variation. Thus, better quality of the hybrid wind power system is achieved.

• Journal of the Korean Institute of Illuminating and Electrical Installation Engineers
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• v.23 no.7
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• pp.29-40
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• 2009

This paper proposes a modeling and controller design approach for a hybrid wind power generation system that considers a fixed wind-turbine and a dump load. Since operating conditions are kept changing, it is challenge to design a control for reliable operation of the overall system To consider variable operating conditions, Takagi-Sugeno (TS) fuzzy model is taken into account to represent time-varying system by expressing the local dynamics of a nonlinear system through sub-systems, partitioned by linguistic rules. Also, each fuzzy model has uncertainty. Thus, in this paper, a modem nonlinear control design technique, the sliding mode nonlinear control design, is utilized for robust control mechanism In the simulation study, the proposed controller is compared with a proportional-integral (PI) controller. Simulation results show that the proposed controller is more effective against disturbances caused by wind speed and load variation than the PI controller, and thus it contributes to a better quality wind-hybrid power generation system.

• Proceedings of the Korean Institute of IIIuminating and Electrical Installation Engineers Conference
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• 2005.11a
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• pp.315-320
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• 2005

This paper is proposed adaptive fuzzy-neuro controller for high performance of induction motor drive. The design of this algorithm based on fuzzy-neural network controller that is implemented using fuzzy control and neural network. This controller uses fuzzy rule as training patterns of a neural network. Also, this controller uses the back-propagation method to adjust the weights between the neurons of neural network in order to minimize the error between the command output and actual output. A model reference adaptive scheme is proposed in which the adaptation mechanism is executed by fuzzy logic based on the error and change of nor measured between the motor speed and output of a reference model. The control performance of the adaptive fuzy-neuro controller is evaluated by analysis for various operating conditions. The results of experiment prove that the proposed control system has strong high performance and robustness to parameter variation, and steady-state accuracy and transient response.

• Proceedings of the KIEE Conference
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• 2005.07b
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• pp.1453-1455
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• 2005

This paper presents self tuning PI controller of IPMSM drive using neural network. Self tuning PI controller is developed to minimize overshoot, rise time and settling time following sudden parameter changes such as speed, load torque and inertia. Also, this paper is proposed speed control of IPMSM using neural network and estimation of speed using artificial neural network(ANN) controller. The results on a speed controller of IPMSM are presented to show the effectiveness of the proposed gain tuner. And this controller is better than the fixed gains one in terms of robustness, even under great variations of operating conditions and load disturbance.

• Advances in concrete construction
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• v.11 no.1
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• pp.1-9
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• 2021

This article discusses the issue of optimizing controller design issues, in which the artificial intelligence (AI) evolutionary bat (EB) optimization algorithm is combined with the fuzzy controller in the practical application of the building. The controller of the system design includes different sub-parts such as system initial condition parameters, EB optimal algorithm, fuzzy controller, stability analysis and sensor actuator. The advantage of the design is that for continuous systems with polytypic uncertainties, the integrated H2/H∞ robust output strategy with modified criterion is derived by asymptotically adjusting design parameters. Numerical verification of the time domain and the frequency domain shows that the novel system design provides precise prediction and control of the structural displacement response, which is necessary for the active control structure in the fuzzy model. Due to genetic algorithm (GA), we use a hierarchical conditions of the Hurwitz matrix test technique and the limits of average performance, Hierarchical Fitness Function Structure (HFFS). The dynamic fuzzy controller proposed in this paper is used to find the optimal control force required for active nonlinear control of building structures. This method has achieved successful results in closed system design from the example.

• Proceedings of the KIEE Conference
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• 1988.11a
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• pp.36-41
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• 1988

Analog and Digital Speed Control Systems have mutually complementary properties. Analog System has good dynamic characteristics and moderate steady-state accuracy and can be implemented economically with operational a ampliers. Digital System, on the contray, has good static accuracy, but relatively poor dynamic property. So, a hybrid system which uses both digital and analog control can have good static and dynamic characteristics. In this paper, it is shown that a simple digital controller can improve steady-state accuracy of existing analog control system satisfactorily, and some design criteria are presented also.

• 제어로봇시스템학회:학술대회논문집
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• 1990.10a
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• pp.96-100
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• 1990

Dynamic modelling and controller design technique for constant-frequency series resonant converter with buck type preregulator are mainly described in this paper. An equivalent circuit model is derived and a state equation is developed from this model. To improve the dynamic performance, a negative feedback of inductor current is added to the proportional and integral control of output voltage. Furthermore, an optimization technique with prescribed eigenvalue region is applied to the determination of feedback gains. With the presented design method, much better dynamic performance can be obtained.

• Proceedings of the KIEE Conference
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• 2007.10a
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• pp.349-350
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• 2007

In general, PID controller is widely used for speed control of motor. PID control method is easy and simple to set the control gains without model parameters. However, the precise and robust control of the motors needs complex and difficult control method. In this paper, we present a simple and robust speed controller for disturbances of a motor. The numerical simulation shows that the proposed control considering disturbances can improve the speed tracking performance more than PI control.