• Journal of Electrical Engineering and Technology
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• v.12 no.4
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• pp.1456-1470
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• 2017

This paper presents sensorless vector control of induction motor drives with an improved model reference adaptive system observer for rotor speed estimation and parameters identification from measured stator currents, stator voltages and estimated rotor fluxes. The aim of the proposed sensorless control method is to compensate simultaneously stator resistance and rotor time constant variations which are subject of large changes during operation. PI controllers have been used in the model reference adaptive system adaptation mechanism and in the closed loops of speed and currents regulation. The stability of the proposed observer is proved by the Lyapunov's theorem and its feasibility is verified by experimentation. The experimental results are obtained with an 1 kW induction motor using Matlab/Simulink and a dSPACE system with DS1104 controller board showing the effectiveness of the proposed approach in terms of dynamic performance.

• 제어로봇시스템학회:학술대회논문집
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• 2004.08a
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• pp.1239-1244
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• 2004

Precise positioning is an important problem facing motion control systems which usually use electric motor. A motor possesses a nonlinear property which degrades the positioning accuracy. Therefore, a compensator which linearizes the relationship between the angular velocity and input signal of the motor is required to enable precise positioning. In this paper, the design of a Model Reference Adaptive Control System (MRACS) for realizing the precise positioning for a system using a motor including the nonlinear property is described. The designed MRACS is applied to the attitude control problem on a satellite using a DC servomotor to drive its reaction wheel. Experimental results demonstrate the validity of a proposed control method for a positioning control system with an electric motor.

• Proceedings of the KIEE Conference
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• 2005.10b
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• pp.570-572
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• 2005

This paper is proposed adaptive hybrid artificial intelligent(HAI) controller for high performance of induction motor drive. The design of this algorithm based on fuzzy-neural network(FNN) 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 error measured between the motor speed and output of a reference model. The control performance of the adaptive FNN 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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• 2004.04a
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• pp.143-146
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• 2004

This paper is proposed adaptive fuzzy-neural network(FNN) controller for speed control of induction motor drive. The design of this algorithm based on FNN controller that is implemented using fuzzy control and neural network. A model reference adaptive scheme is proposed in which the adaptation mechanism is executed by fuzzy logic based on the error and change of error measured between the motor speed and output of a reference model. The control performance of the adaptive FNN controller is evaluated by analysis for various operating conditions.

• Journal of Electrical Engineering and Technology
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• v.9 no.3
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• pp.873-881
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• 2014

Speed sensorless modes of operation are becoming standard solution in the area of electric drives. This paper presents flux estimator and speed estimator for the speed sensorless vector control of induction motors. The proposed sensorless methods are based on the model reference adaptive system (MRAS) observer and adaptive speed observer (ASO). The proposed speed estimation algorithm can be employed in the power control of grid connected induction generator for wind power applications. Two proposed schemes are verified through computer simulation PSIM and compared their simulation results.

• 제어로봇시스템학회:학술대회논문집
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• 2001.10a
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• pp.148.5-148
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• 2001

In this report, an adaptive flight control law based on a linear-parameter-varying (LPV) model is presented for a flight control system. The control system is designed to track an output of a vehicle to a reference signal from the guidance system, which generates a reference flight path. The proposed adaptive control law adjusts the controller gains continuously on line as flight conditions change. The obtained adaptive controller guarantees global stability over a wide flight envelope. Computer simulation involving six-degree-of-freedom nonlinear flight dynamics is applied to Japan´s automatic landing flight experimental vehicle (ALFLEX) to examine the effectiveness of the proposed adaptive flight control law.

• Journal of the Korean Institute of Telematics and Electronics
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• v.20 no.1
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• pp.37-44
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• 1983

A class of adaptive control algorithms applied to discrete-time single-input single-output deterministic linear systems is analyzed by using direct model reference adaptive control. Controller parameters are identified with weighted least square Method. And computer simulations reveal that proposed weighted least square method in which the value of depends on the identification error can be used regardless of the sufficient condition of reference input signal.

• Transactions of the Korean Society of Mechanical Engineers A
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• v.28 no.8 s.227
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• pp.1152-1158
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• 2004

In this study a MRAC(model reference adaptive control) for fluid power elevator model system was designed. The MRAC was compared with PI control in case of applying to the elevator model system with constant external load and changing external load. In this case external load was produced by a single fluid power cylinder combined with pressure control valve. In conclusion the MRAC control performance was better than PI control performance because overshoot and steady state error of the elevator model system controlled by the MRAC were not appeared for constant and changing external load.

• The Transactions of the Korean Institute of Electrical Engineers B
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• v.48 no.3
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• pp.124-130
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• 1999

Speed and position sensors require the additional mounting space, reduce the reliability in harsh environments and increase the cost of motor. Various control algorithms have been proposed for the elimination of speed senor. This paper investigates a novel speed sensorless control of induction motor. The proposed control strategy is based on MRAS(Model Reference Adaptive System) using state observer as a reference model for flux estimation. This algorithm may overcome several shortages of conventional MRAS: integrator problems, small EMF at low speed and relatively large sensitivity to resistance variation. The proposed algorithm is verified through simulation and experiment.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.56 no.1
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• pp.183-187
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• 2007

This paper proposes Fuzzy Speed Estimator using Fuzzy Logic Controller(FLC) as a adaptive law in Model Reference Adaptive System(MRAS) in order to realize the speed-sensorless control of an induction motor. Fuzzy Speed Estimator estimates the speed of an induction motor with a rotor flux of the reference model and the adjustable model in MRAS. Fuzzy logic controller reduces the error of the rotor flux between the reference model and the adjustable model using the error and the change of error of the rotor flux as the input of FLC. The experiment is executed to verify the propriety and the effectiveness of the proposed speed estimator.