• Advances in Computational Design
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• v.8 no.3
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• pp.237-253
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• 2023

Torque ripple content and variable switching frequency operation of conventional direct torque control (DTC) are reduced by the integration of space vector modulation (SVM) into DTC. Integration of space vector modulation to conventional direct torque control known as SVM-DTC. It had been more frequently used method in renewable energy and machine drive systems. In this paper, SVM-DTC is used to control the rotor side converter (RSC) of a wind driven doubly-fed induction generator (DFIG) because of its advantages such as reduction of torque ripples and constant switching frequency operation. However, flux and torque ripples are still dominant due to distorted current waveforms at different operations of the wind turbine. Therefore, to smoothen the torque profile a Neural Network Controller (NNC) based SVM-DTC has been proposed by replacing the PI controller in the speed control loop of the wind turbine controller. Also, stability analysis and simulation study of DFIG using process reaction curve method (RRCM) are presented. Validation of simulation study in MATLAB/SIMULINK environment of proposed wind driven DFIG system has been performed by laboratory developed prototype model. The proposed NNC based SVM-DTC yields superior torque response and ripple reduction compared to other methods.

• Journal of Power Electronics
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• v.9 no.3
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• pp.491-498
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• 2009

The hydraulic-oil pump is widely used for building machinery, brake systems of vehicles and automatic control systems due to its high dynamic force and smooth linear force control performance. This paper presents a novel direct instantaneous pressure control of the hydraulic pump system with SRM drive. The proposed hydraulic pump system embeds the pressure controller and direct instantaneous torque controller. Due to the proportional relationship between pump pressure and torque, pressure can be controlled by the motor torque directly. The proposed direct torque controller can reduce inherent torque ripple of SRM, and develop a smooth torque, which can increase the stability of the hydraulic pump. The proposed hydraulic pump system has also fast step response and load response. The proposed hydraulic pump system is verified by computer simulation and experimental results.

• Journal of information and communication convergence engineering
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• v.6 no.4
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• pp.421-424
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• 2008

In torque transmission system, we must suppressed vibration for Accuracy characteristic response of motor, Therefore, vibration suppression factor is very important motor control. To suppress vibration, a various control method has been proposed. Specially, one method of vibration suppression used disturbance observer filter. This method is torsion torque passing disturbance observer filter. By the estimated torsion torque feedback, vibration can be suppressed. The CDM(coefficient diagram method) is used to design the filter and Proportional controller. But using coefficient diagram method, not adapted controller parameter in disturbance. For this solution, we used fuzzy controller for auto tuning controller parameter. We proved this approach is confirmed by simulation.

• Journal of the Korean Society for Precision Engineering
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• v.13 no.10
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• pp.123-129
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• 1996

Computed torque method has been used for precise trajectory control of the robotic system that involves nonlinear dynamics. It is hard to know exact values of robot system parameters, and the robot arm receives umpredictable interference from the working environment. These disturbances, especially in a direct drive robot, are directly transmitted to actuating motor without reduction. Modelling error and distrubance can cause significant errors in a trajectory tracking problem. In this paper, we propose a new controller that $H_{\infty}$controller is conbined to robot system linearized by computed torque. Simula- tions are made for comparing the performance of the proposed controller with that of a nonlinear $H_{\infty}$ controller proposed by Chen and also computed torque method.hod.

• 제어로봇시스템학회:학술대회논문집
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• 2005.06a
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• pp.514-518
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• 2005

In this paper an adaptive fuzzy PI controller with feedback linearizing meth od is implemented to controlling flux and torque separately in induction motor. In this paper first decoupling of torque and flux which are outputs to be controlled, is achieved by using feedback linearization methodology. Then for reducing the effect of noise and rejection of disturbance, main part of controller which is adaptive PI fuzzy controller, is designed. Coefficients of PI controller are determined by defined fuzzy rules due to error dynamic. Inputs of fuzzy system are defined sliding surfaces which consist of torque and flux errors. The main contribution of this paper is effect reduction of noise and disturbance on torque and flux which is based on fuzzy logic and nonlinear control. At last the effectiveness of the proposed control scheme in presence of noise and load disturbance is simulated and comprised to applying sliding method. The results verify better effectiveness of the proposed method for effect reduction of noise and disturbance.

• Proceedings of the KIEE Conference
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• 2005.10b
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• pp.566-569
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• 2005

Interior permanent magnet synchronous motor(IPMSM) has become a popular choice in electric vehicle applications, due to their excellent power to weight ratio. The paper is proposed maximum torque control of IPMSM drive using artificial intelligent(AI) controller. 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-axis current $i_d$ for maximum torque operation is derived. This paper considers the design and implementation of novel technique of high performance speed control for IPMSM using AI controller. This paper is proposed speed control of IPMSM using learning mechanism fuzzy neural network(LM-FNN) and estimation of speed using artificial neural network(ANN) controller. The back propagation neural network technique is used to provide a real time adaptive estimation of the motor speed. The proposed control algorithm is applied to IPMSM drive system controlled LM-FNN and ANN controller, the operating characteristics controlled by maximum torque control are examined in detail. Also. this paper is proposed the experimental results to verify the effectiveness of AI controller.

• The Transactions of the Korean Institute of Electrical Engineers B
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• v.55 no.2
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• pp.89-97
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• 2006

Interior permanent magnet synchronous motor(IPMSM) has become a popular choice in electric vehicle applications, due to their excellent power to weight ratio. The paper is proposed maximum torque control of IPMSM drive using learning mechanism-fuzzy neural network(LM-FNN) 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-axis current $i_{d}$ for maximum torque operation is derived. This paper considers the design and implementation of novel technique of high performance speed control for IPMSM using LM-FNN controller and ANN controller. The hybrid combination of neural network and fuzzy control will produce a powerful representation flexibility and numerical processing capability. Also, this paper is proposed speed control of IPMSM using LM-FNN and estimation of speed using ANN controller. The back propagation neural network technique is used to provide a real time adaptive estimation of the motor speed. The proposed control algorithm is applied to IPMSM drive system controlled LM-FNN 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 LM-FNN and ANN controller.

• The Transactions of the Korean Institute of Electrical Engineers D
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• v.55 no.3
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• pp.110-114
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• 2006

Interior permanent magnet synchronous motor(IPMSM) has become a popular choice in electric vehicle applications, due to their excellent power to weight ratio. In this paper maximum torque control of IPMSM drive using artificial intelligent(AI) controller is proposed. 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-axis current $i_d$ for maximum torque operation is derived. This paper considers the design and implementation of novel technique of high performance speed control for IPMSM using AI controller. This paper is proposed speed control of IPMSM using adaptive learning mechanism fuzzy neural network(ALM-FNN) and estimation of speed using artificial neural network(ANN) controller. The back propagation neural network technique is used to provide a real time adaptive estimation of the motor speed. The proposed control algorithm is applied to IPMSM drive system controlled ALM-FNN and ANN controller, the operating characteristics controlled by maximum torque control are examined in detail. Also, this paper is proposed the experimental results to verify the effectiveness of AI controller.

• Transactions of the Korean Society of Mechanical Engineers A
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• v.24 no.3 s.174
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• pp.718-727
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• 2000

Nowadays, most passenger cars equipped with automatic transmissions use torque converter clutches to reduce fuel consumption, and recently the slip control scheme of torque converter clutches is widely studied for the expansion of the operating region of torque converter clutches and thus for the further improvement of the fuel economy of vehicles. In this study, the analysis of the torque converter clutch system including the line pressure control unit of the automatic transmission and the actuating hydraulic control unit of the torque converter clutch is performed, and a feedforward controller and a fuzzy logic controller for its slip control are proposed. Also, for the slip controller to use the grade resistance information during control, an observer-based grade resistance estimator is designed. The performance of the designed grade resistance estimator and the slip controller is verified by dynamic simulations, and the effect of the torque converter clutch slip control on the fuel economy is examined using a driving cycle simulation.

• The Transactions of the Korean Institute of Power Electronics
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• v.15 no.4
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• pp.282-287
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• 2010

This paper proposes a new nonlinear speed controller with a fuzzy load torque observer based on the Takagi-Sugeno fuzzy method for a permanent magnet synchronous motor(PMSM). The LMI conditions are derived for the existence of the proposed nonlinear speed controller and fuzzy load torque observer, and the LMI parameterization to obtain the gain matrices of the controller and observer is given. In this paper, to verify the performance of the proposed nonlinear speed controller and fuzzy load torque observer, and the simulation and experimental results are demonstrated under motor parameter and load torque variations.