• The Transactions of the Korean Institute of Electrical Engineers D
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• v.54 no.11
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• pp.650-653
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• 2005

In this paper, adaptive high-order neural network controller(AHONNC) is adopted to control an induction servomotor. A algorithm is developed by combining compensation control and high-order neural networks. Moreover, an adaptive bound estimation algorithm was proposed to estimate the bound of approximation error. The weight of the high-order neural network can be online tuned in the sense of the Lyapunov stability theorem; thus, the stability of the closed-loop system can be guaranteed. Simulation results for induction servomotor drive system are shown to confirm the validity of the proposed controller.

• Proceedings of the KIEE Conference
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• 2003.11c
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• pp.903-905
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• 2003

In this paper, adaptive high-order neural network controller(AHONNC) is adopted to control of an induction servomotor. A algorithm is developed by combining compensation control and high-order neural networks. Moreover, an adaptive bound estimation algorithm was proposed to estimate the bound of approximation error. The weight of the high-order neural network can be online tuned in the sense of the Lyapunov stability theorem; thus, the stability of the closed-loop system can be guaranteed. Simulation results for induction servomotor drive system are shown to confirm the validity of the proposed controller.

• The Transactions of the Korean Institute of Electrical Engineers
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• v.41 no.5
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• pp.474-482
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• 1992

This paper describes the speed control system of an induction type ac servomotor drive on the vector control basis of slip frequency and constant secondary flux control for a quick torque response. The system is composed of a digital controller using a SCB-V50 microprocessor and a PWM inverter with power MOSFETs for high speed switching. And, for the measurement of actual instantaneous currents, MDCS A070-051 hall sensors are employed. The rising time of step responce by this system through the test of a 600[W] ac servomotor is 30[ms]. Overall experimental result shows that the drive performance of the system is similar to that of a separately excited armature current control of a dc motior.

• Proceedings of the KIEE Conference
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• 2006.07d
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• pp.1834-1835
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• 2006

A robust adaptive position control algorithm is proposed for servomotor drive system with uncertainties and load disturbance. The proposed controller is comprised of a nominal controller and a robust control. The nominal controller is designed in the condition without all the external load disturbance, nonlinear friction and unpredicted uncertainties. The robust controller containing lumped uncertainty approximator using fuzzy-neural network(FNN) is designed to dispel the effect of uncertainties and load disturbance. The interconnection weight of the FNN can be online tuned in the sense of the Lyapunov stability theorem thus asymptotic stability of the proposed control system can be guaranteed. Finally, simulation results verify that the proposed control algorithm can achieve favorable tracking performance for the induction servomotor drive system.

• The Transactions of the Korean Institute of Electrical Engineers
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• v.38 no.12
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• pp.1041-1047
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• 1989

In recent years, a.c servomotors have been gradually replacing d.c servomotors in various high-performance applications such as machine tools and industrial robots. Inparticular, the high performance slip-frequency control of an induction motor, which is often called the vector control, is considered ane of th ebest a.c drives. In this paper, the transient state equations and vector control algorithms of an induction type servomotor are described mathematically by using the two- axis theory (d-q coordinates). According to the result of these algorithms, we scheme the speed control system for the motor in which the vector control is adopted to give high performance. Motor drive through a PWM inverter with power MOSFET is controlled so that the actual input current to the motor may track the current reference obtained from a micro-computer (8086 CPU). Driving experiments are performed in the range of 0 to 3000 rpm, and it is verified that high speed response is obtained for this system.