• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2001.10a
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• pp.397-400
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• 2001

This paper proposed a speed adaptive control system with load torque observer and feed-forward compensation using neural network for air compressor system driven an induction motor. The motor receive impact load change under the influence of piston movement of up and down, and so it difficult to obtain good speed control characteristics. With real-time adjusting control gain estimated in neural network, control characteristics of motor is improved. The validity of the proposed system is confirmed through the theoretical analysis and computer simulation.

• The Transactions of the Korean Institute of Power Electronics
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• v.3 no.3
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• pp.206-213
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• 1998

In this paper, an adaptive compensation technique for parameter variation is proposed which can perform quick torque response in vector control of an induction motors. To solve the problem of control performance degradation due to parameter variation in an induction motor, a rotor resistance estimation is performed by the model reference adaptive control(MRAC). The algorithm of rotor resistance estimation is composed of the error relationship which is generated between a motor real instantaneous reactive power and an estimated instantaneous reactive power. The advantage of such a real reactive power reference model is independence of the motor parameter variation. The estimation rotor resistance values are applied to the direct vector control system with a flux observer. Finally, the simulations and experiment are presented to validate the rotor resistance estimation algorithm of induction motor.

• The Transactions of the Korean Institute of Power Electronics
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• v.10 no.1
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• pp.52-60
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• 2005

Generally, a voltage difference or voltage distortion exists between the reference voltage and the practical voltage applied to a motor in a pulse width modulated(PWM) voltage source inverter(VSI). This voltage distortion varies with the operating conditions such as the temperature, DC link voltage, and phase current level. Also the voltage distortion affects the machine current distortion, torque pulsations, and control performance. In this paper, the voltage distortion in a PWM VSI is analyzed and a new on-line estimation method based on the model reference adaptive system(MRAS) is proposed to compensate the time varying voltage distortion, while considering the parameter variations for a permanent magnet synchronous motor (PMSM). The simulation and experimental results show the effectiveness of the proposed voltage difference observer and the compensation method.

• Proceedings of the KIEE Conference
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• 2007.10a
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• pp.293-294
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• 2007

This paper addresses the adaptive controller for efficiency optimization of induction motors. The paper describes an adaptive controller based on-line efficiency optimization control of a drive that uses a direct vector controlled induction motors. To improve the efficiency of the induction motors, it is important to find the optimal flux reference that minimize power loss. The proposed optimal flux reference is derived using a power loss function that is constructed with stator resistance losses, rotor resistance losses and core losses. The proposed sliding mode flux observer generates estimates the unmeasured rotor fluxes. An optimal efficiency controller has goal of maximizing the efficiency for a given speed and load torque. A simulation shows the effectiveness of the proposed technique.

• Journal of Power Electronics
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• v.19 no.3
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• pp.771-783
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• 2019

Due to nonlinear-synthetic uncertainty including the total unknown nonlinear load torque, the total parameter variation and the fixed load torque, a synchronous reluctance motor (SynRM) driving a continuously variable transmission (CVT) system causes a lot of nonlinear effects. Linear control methods make it hard to achieve good control performance. To increase the control performance and reduce the influence of nonlinear time-synthetic uncertainty, an admixed recurrent Gegenbauer orthogonal polynomials neural network (ARGOPNN) with a modified particle swarm optimization (MPSO) control system is proposed to achieve better control performance. The ARGOPNN with a MPSO control system is composed of an observer controller, a recurrent Gegenbauer orthogonal polynomial neural network (RGOPNN) controller and a remunerated controller. To insure the stability of the control system, the RGOPNN controller with an adaptive law and the remunerated controller with a reckoned law are derived according to the Lyapunov stability theorem. In addition, the two learning rates of the weights in the RGOPNN are regulating by using the MPSO algorithm to enhance convergence. Finally, three types of experimental results with comparative studies are presented to confirm the usefulness of the proposed ARGOPNN with a MPSO control system.

• Proceedings of the KIEE Conference
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• 2007.04c
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• pp.180-182
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• 2007

An interior permanent magnet synchronous motors (IPMSM) are receiving increased attention for many industrial applications because of its high torque to inertia ratio, superior power density, and high efficiency. This paper presents algorithm for speed sensorless control based on an adaptive binary observer adding the fuzzy gain compensator. Effectiveness of algorithm is confirmed by the experiments.

• The Transactions of the Korean Institute of Electrical Engineers B
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• v.52 no.11
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• pp.541-547
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• 2003

Speed and torque controls of permanent magnet synchronous motors are usually attained by the application of position and speed sensors. However, speed and position sensors require the additional mounting space, reduce the reliability in harsh environments and increase the cost of a motor. Therefore, many studies have been peformed for the elimination of speed and position sensors. This paper investigates a novel speed sensorless control of a permanent magnet synchronous motor. The proposed control strategy is based on the MRAS(Model Reference Adaptive System) using the state observer model with the current error feedback and the magnet flux model as two models for the back-emf estimation. The proposed algorithm is verified through the simulation and experiment.

• Journal of the Institute of Electronics Engineers of Korea SC
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• v.44 no.3
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• pp.64-69
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• 2007

Motion control of the system is a position control of motor. Motion control of an uncertain robot system is considered as one of the most important and fundamental research directions in the robotics. Some distinguished works using linear control, adaptive control, robust control strategies based on computed torque methodology have been reported. However, it is generally recognized within the control community that these strategies suffer from the following problems : the exact robot dynamics are needed and hard to implement, the adaptive control cannot guarantee the performance during the transient period for adaptation under the variation, the robust control algorithms such as the sliding mode control need information on the bounds of the possible uncertainty and disturbance. And it produces a large control input as well. In this dissertation, a motion control for the unmanned intelligent robot system using disturbance observer is studied. This system is affected with an impact vibration disturbance. This paper describes a stable motion control of the system with the consideration of external disturbance. To obtain the stable motion independently against the external disturbance, the disturbance rejection is strongly required. To address the above issue, this paper presents a Disturbance OBserver(DOB) control algorithm. The validity of the suggested DOB robust control scheme is confirmed by several computer simulation results. And the experiments with a motor system is performed to give the validity of applicability in the industrial field. This results make the easier implementation of the controller possible in the field.

• Journal of Power Electronics
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• v.15 no.5
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• pp.1244-1255
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• 2015

This paper presents a new multi-machine robust control based on an electric differential system for electric vehicle (EV) applications which is composed of four in-wheel permanent magnet synchronous motors. It is based on a new master-slave direct torque control (DTC) algorithm, which is used for the control of bi-machine traction systems based on a speed model reference adaptive system observer. The use of an electric differential in the design of a new EV constitutes a technological breakthrough. A classical system with a multi-inverter and a multi-machine comprises a three-phase inverter for each machine to be controlled. Another approach consists of only one three-phase inverter for several permanent magnet synchronous machines. The control of multi-machine single-inverter systems is the subject of this study. Several methods have been proposed for the control of multi-machine single-inverter systems. In this study, a new master-slave based DTC strategy is developed to generate an electric differential system. The entire system is simulated by Matlab/Simulink. The simulation results show the effectiveness of the new multi-machine robust control based on an electric differential system for use in EV applications.