• 제어로봇시스템학회:학술대회논문집
• /
• 2004.08a
• /
• pp.1915-1919
• /
• 2004

In this paper, we propose a new discrete-time predictive current controller for a PMSM(Permanent Magnet Synchronous Motor). The main objectives of the current controllers are to ensure that the measured stator currents tract the command values accurately and to shorten the transient interval as much as possible, in order to obtain high-performance of ac drive system. The conventional predictive current controller is hard to implement in full digital current controller since a finite calculation time causes a delay between the current sensing time and the time that it takes to apply the voltage to motor. A new control strategy in this paper is seen the scheme that gets the fast adaptation of transient current change, the fast transient response tracking and is proposed simplified calculation. Moreover, the validity of the proposed method is demonstrated by numerical simulations and the simulation results will be verified the improvements of predictive controller and accuracy of the current controller.

• Journal of the Korean Society of Industry Convergence
• /
• v.2 no.2
• /
• pp.91-102
• /
• 1999

The paper describes a robust adaptive control algorithm for induction motor drive without speed sensor at low speed range. The control algorithm use only current sensors in a space vector pulse width modulation within loop control with rotor speed estimation and voltage source inverter. On-line rotor speed estimation is based on utilizing parallel model reference adaptive control system. MRAC of the modified flux model for flux and rotor speed estimator uses dual-adaptation mechanism, ${\omega}_r$ and ${\omega}_e$ scheme. The estimated flux components in the model can be compensated from the effects of offset errors on pure integrals. It can be compensated to the parameter variations and torque fluctuation with speed estimation in less then 10 rad/sec. In a simulation, the proposed induction motor control algorithm without speed sensor at very low speed range are shown to operate very well in spite of variable rotor time constant and fluctuating load without change the controller parameters. The suggested control strategy and estimation method have been validated by simulation study, and it proposed the designed system for the implementation using TI320C31 DSP/ASIC controller.

• Journal of the Korean Institute of Illuminating and Electrical Installation Engineers
• /
• v.21 no.5
• /
• pp.32-43
• /
• 2007

Interior permanent magnet synchronous motor(IPMSM) has become a popular choice in electric vehicle applications, due to their excellent power to weight ratio. This paper proposes maximum torque control of IPMSM drive using adaptive learning fuzzy neural network and artificial neural network. This control method is applicable over the entire speed range which considered the limits of the inverter's current and voltage rated value. This paper proposes speed control of IPMSM using adaptive learning fuzzy neural network and estimation of speed using artificial neural network. 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 adaptive learning fuzzy neural network and artificial neural network, the operating characteristics controlled by maximum torque control are examined in detail. Also, this paper proposes the analysis results to verify the effectiveness of the adaptive learning fuzzy neural network and artificial neural network.

• Journal of Power Electronics
• /
• v.19 no.5
• /
• pp.1193-1202
• /
• 2019

A SMPMSM drive system is a typical nonlinear system with time-varying parameters and unmodeled dynamics. The speed outer loop and current inner loop control structures are coupled and coexist with various disturbances, which makes the speed control of SMPMSM drive systems challenging. First, an ultra-local model of a PMSM driving system is established online based on the algebraic estimation method of model-free control. Second, based on the backstepping control framework, model-free adaptive integral backstepping (MF-AIB) control is proposed. This scheme is applied to the permanent magnet synchronous motor (PMSM) drive system of an electric vehicle for the first time. The validity of the proposed control scheme is verified by system simulations and experimental results obtained from a SMPMSM drive system bench test.

• Proceedings of the KIEE Conference
• /
• 2008.04a
• /
• pp.219-220
• /
• 2008

The paper is proposed maximum torque control of SynRM drive using adaptive fuzzy neuro inference system(AFNIS) 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. The proposed control algorithm is applied to SynRM drive system controlled AFNIS 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 AFNIS and ANN controller.

• Proceedings of the KIEE Conference
• /
• 1994.07a
• /
• pp.389-391
• /
• 1994

This paper proposes an approach to control the current amplitude and phase simultaneously. To do this, variable structure control and adaptive parameter estimation arc applied to the current control of a single-phase PWM inverter with unknown R-L series load. The load parameters, R and L, are estimated by using the recursive least square method and these parameters are used to adjust the feedback gains of control input. The inverter system is modelled in a 2nd-order system by treating load current variation caused by inductive component as a disturbance. Simulation and experiment based on the 2nd -order model are done and the results show good dynamic response and low THD.

• The Transactions of the Korean Institute of Electrical Engineers P
• /
• v.51 no.1
• /
• pp.18-27
• /
• 2002

This paper presents an implementation of digital high-performance speed sensorless control system of an induction motor drives with Direct Torque Control(DTC). The system consists of closed loop stator flux and torque observer, speed and torque estimators, two hysteresis controllers, an optimal switching look-up table, IGBT voltage source inverter, and TMS320C31 DSP controller board. The stator flux observer is based on the combined current and voltage model with stator flux feedback adaptive control for wide speed range. The speed estimator is using the model reference adaptive system(MRAS) with rotor flux linkages for speed turning signal estimation. In order to prove the suggested speed sensorless control algorithm, and to obtain a high-dynamic robust adaptive performance, we have some simulations and actual experiments at low(20rpm) and high(1000rpm) speed areas. The developed speed sensorless system are shown a good speed control response characteristic, and high performance features using 2.2[kW] general purposed induction motor.

• Proceedings of the KIEE Conference
• /
• 1989.07a
• /
• pp.591-595
• /
• 1989

The application of Variable Structure System (VSS) to the position control of a Permanent Magnet Synchronus Motor is discussed. VSS is expected to be a powerful and potential tool to construct new control strategy for ac machines, since the resulting system shows robust performance to parametic variations and disturbances. An adaptive VSS which can make corrections or adjustments in the parameters of the control device of the VSS in accordance with the current values of the plant parameters and the constraints on the control is preposed. Various simulation results are reported to show the validity of the proposed control method.

• 제어로봇시스템학회:학술대회논문집
• /
• 1998.10a
• /
• pp.419-422
• /
• 1998

This paper presents how to design speed control of wound rotor induction motors with slip energy recovery. The speed is limited at some range of sub-synchronous speed of the rotating magnetic field. The problem with speed control by adjusting resistance value in the rotor circuit reduces the efficiency of power, because of the slip energy is lost when it passes through the rotor resistance. The control system is designed to maintain efficiency of motor, where it recovers loss energy by returning it to the system to improve the efficiency. A new PI control method of adaptive control [1］,［13］is applied for the system with cascade type PI controller on the main loop to keep the speed constant and the internal loop to adjust the rotor appropriated current of the load provides the good transient response without overshoot.

• Journal of Communications and Networks
• /
• v.6 no.2
• /
• pp.133-146
• /
• 2004

Recently, many active queue management (AQM) algorithms have been proposed to address the performance degradation. of end-to-end congestion control under tail-drop (TD) queue management at Internet routers. However, these AQM algorithms show performance improvement only for limited network environments, and are insensitive to dynamically changing network situations. In this paper, we propose an adaptive queue management algorithm, called PID-controller, that uses proportional-integral-derivative (PID) feedback control to remedy these weak-Dalles of existing AQM proposals. The PID-controller is able to detect and control congestion adaptively and proactively to dynamically changing network environments using incipient as well as current congestion indications. A simulation study over a wide range of IP traffic conditions shows that PID-controller outperforms other AQM algorithms such as Random Early Detection (RED) [3] and Proportional-Integral (PI) controller [9] in terms of queue length dynamics, packet loss rates, and link utilization.