• Journal of Institute of Control, Robotics and Systems
• /
• v.7 no.2
• /
• pp.109-116
• /
• 2001

The paper presents a speed control algorithm for a full pitch-controlled wind turbine system. Torque of a blade generated by wind energy is a nonlinear function of wind speed, angular velocity, and pitch angle of the blade. The design of the controller, in general, is performed by linearizing the torque in the vicinity of the operating point assuming the angular velocity of the blade is constant. For speed control, however the angular velocity is on longer a constant, so that linearization of the torque in terms of wind speed and pitch angle is impossible. In this study, a reference pitch model is derived in terms of a wind speed, angular velocity, and pitch angle, which makes it possible to design a controller without linearizing the nonlinear torque model of the blade. This paper also suggests a method of designing a hydraulic control system for changing the pitch angle of the blade.

• Proceedings of the KSME Conference
• /
• 2004.04a
• /
• pp.739-744
• /
• 2004

This paper presents an adaptive approach to control the amount of slip of the torque converter bypass clutch using its estimated friction coefficient. The proposed approach can be readily implemented using the inexpensive speed sensors currently installed in an automobile. A measurement feedback control law to drive the slip error to zero together with an adaptation law to identify the unknown friction coefficient is developed using the Lyapunov control design method. The robustness of the control and adaptation laws to parametric and/or torque uncertainties as well as the convergence of the friction coefficient are investigated. Simulation results verify the viability of the proposed control algorithm in real-world vehicle control applications.

• Proceedings of the KIEE Conference
• /
• 2005.10b
• /
• pp.566-569
• /
• 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 D
• /
• v.55 no.3
• /
• pp.110-114
• /
• 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.

• Journal of the Korean Institute of Illuminating and Electrical Installation Engineers
• /
• v.19 no.8
• /
• pp.85-93
• /
• 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 posed maximum torque control of IPMSM for high speed drive. 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. The proposed control algorithm is applied to IPMSM drive system for high speed drive, the operating characteristics controlled by maximum torque control are examined in detail by experiment.

• Journal of Institute of Control, Robotics and Systems
• /
• v.20 no.11
• /
• pp.1147-1152
• /
• 2014

This paper describes the design of a four-axis force/torque sensor with PSPBs (Parallel Step Plate Beams). The sensor is composed of eight PSPBs, a force/torque transmitting block, and fixing blocks. It is designed by using the FEM(Finite Element Method), and fabricated by using strain gages. The characteristic tests of the sensor are carried out, and the interference error, repeatability error, and non-linearity error are less than 2.21%, 0.03% and 0.03%. Furthermore, the structure of the four-axis force/torque sensor with PSPBs has a larger rated capacity than that of the four-axis force/torque sensor with PPBs under the same overall sensor size and the same rated output. It is thought that the developed four-axis force/torque sensor with PSPBs can be used for measuring the forces and torques in an intelligent robot, automation devices, etc.

• The Transactions of the Korean Institute of Power Electronics
• /
• v.17 no.2
• /
• pp.135-141
• /
• 2012

PMSM(Permanent Magnet Synchronous Motor) has periodic torque ripple from the cogging torque and load conditions. This paper proposes the modified PID speed controller to reduce the speed ripple of the PMSM. The proposed modified PID controller uses a selective D(Differential) control term according to the speed error and the differential of the speed error. The proposed speed controller produces an additional torque reference such as torque compensator based on PI controller according to the speed error and the differential of the speed error, and it can reduce the vibration of the conventional D-control term with reduced speed ripple. Since the additional torque reference of the proposed speed controller is changed by the sign of the speed error and the differential of the speed error, a simple function to determine the sign of the error is used to produce the compensated torque. The proposed control scheme is verified by the computer simulation and the experiments.

• Journal of Electrical Engineering and Technology
• /
• v.12 no.6
• /
• pp.2289-2297
• /
• 2017

Applying a periodic load torque to an AC motor generates a ripple, which is synchronized to the frequency of the periodic load torque, at the speed of the motor. Consequently, numerous studies have focused on reducing the speed ripple caused by the load torque. However, it is difficult to reduce the speed ripple when there is a time delay in acquiring speed information, such as that from a sensorless control. Therefore, we propose a speed control method for reducing speed ripples caused by a periodic load torque when there is a time delay in acquiring the speed information. The proposed method is verified by conducting simulations using the Simulink program from MATLAB, and by applying the method to an actual motor in which speed ripples occur due to a periodic load torque that is synchronized with the speed of the motor.

• The Transactions of the Korean Institute of Electrical Engineers P
• /
• v.54 no.1
• /
• pp.20-29
• /
• 2005

This paper presents an implementation of efficiency optimization of reluctance synchronous motor (RSM) using a neural network (NN) with a direct torque control (DTC). The equipment circuit considered with iron losses in RSM is analyzed theoretically, and the optimal current ratio between torque current and exiting current component are derived analytically. For the RSM driver, torque dynamic can be maintained with DTC using TMS320F2812 DSP Controller even with controlling the flux level because a torque is directly proportional to the stator current unlike induction motor. In order to drive RSM at maximum efficiency and good dynamics response, the Backpropagation Neural Network is adapted. The experimental results are presented to validate the applicability of the proposed method. The developed control system show high efficiency and good dynamic response features with 1.0 [kW] RSM having 2.57 inductance ratio of d/q.

• 제어로봇시스템학회:학술대회논문집
• /
• 1996.10b
• /
• pp.1087-1090
• /
• 1996

The objective of this paper is to present a new input torque shaping method for slewing and vibration suppression of flexible structure based on Fourier series expansion. Vibration energy of the structure with shaped control input is investigated with respect to the shaping parameter of the reference torque, maneuver time and the number of trigonometric functions to be included in the series. Analytic expressions of the performance indices and their derivatives are derived in the modal coordinates. Numerical results show the effectiveness of the proposed approach to design the open-loop control law that modifies the shape of input torque for simultaneous slewing and vibration suppression.