• Journal of Power Electronics
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• v.5 no.2
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• pp.129-141
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• 2005

A high-performance robust hybrid speed controller for a permanent-magnet synchronous motor (PMSM) drive with an on-line trained neural-network model-following controller (NNMFC) is proposed. The robust hybrid controller is a two-degrees-of-freedom (2DOF) integral plus proportional & rate feedback (I-PD) with neural-network model-following (NNMF) speed controller (2DOF I-PD NNMFC). The robust controller combines the merits of the 2DOF I-PD controller and the NNMF controller to regulate the speed of a PMSM drive. First, a systematic mathematical procedure is derived to calculate the parameters of the synchronous d-q axes PI current controllers and the 2DOF I-PD speed controller according to the required specifications for the PMSM drive system. Then, the resulting closed loop transfer function of the PMSM drive system including the current control loop is used as the reference model. In addition to the 200F I-PD controller, a neural-network model-following controller whose weights are trained on-line is designed to realize high dynamic performance in disturbance rejection and tracking characteristics. According to the model-following error between the outputs of the reference model and the PMSM drive system, the NNMFC generates an adaptive control signal which is added to the 2DOF I-PD speed controller output to attain robust model-following characteristics under different operating conditions regardless of parameter variations and load disturbances. A computer simulation is developed to demonstrate the effectiveness of the proposed 200F I-PD NNMF controller. The results confirm that the proposed 2DOF I-PO NNMF speed controller produces rapid, robust performance and accurate response to the reference model regardless of load disturbances or PMSM parameter variations.

• 제어로봇시스템학회:학술대회논문집
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• 1992.10b
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• pp.140-145
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• 1992

This paper presents a decentralized model reference adaptive control scheme for an interconnected linear system composed of a number of single-input single-output subsystems in which outgoing interactions pass through the measurement channel and are subjected to bounded external disturbances. The scheme can treat the unknown strength of interactions as well as uncertainties in subsystem dynamics, and allows for the case when the relative degree of each decoupled subsystem does not exceed two.

• Journal of the Korean Society of Manufacturing Process Engineers
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• v.7 no.3
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• pp.103-109
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• 2008

The work to raise the bridge plate by using two hydraulic cylinders is very dangerous when generating the unbalance state between cylinders. For solving this problem, one cylinder is forced to follow the trajectory of another cylinder instead of applying the same trajectory to two cylinders at once. In this paper, the control method for dynamic stable on lifting the bridge plate is proposed. The simulation model is derived by using commercial software, AMESim and MatLab/simulink. The PID controller is designed on one cylinder for following the reference trajectory and the adaptive controller is designed on another cylinder for tracking the displacement of one cylinder. The performance improvement is shown by comparing the simulation results through computer simulation.

• Proceedings of the KIPE Conference
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• 1998.10a
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• pp.768-773
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• 1998

The speed control of SRM(Switched Reluctance Motor) needs the accurate position and speed data of rotor. This information is generally provided by a shaft encoder or resolver. In some cases, the environment is which the motor operates may cause difficulties in maintaining the satisfactory position detection performance. Therefore, the elimination of the position and speed sensor has gained wide attention. In this paper, a new algorithm for estimation of rotor position and speed is described for the SRM drives. This method uses is nonlinear adaptive observer using the MRAS(Model Reference Adaptive System). The observer is proved by Lyapunov Stability Theory. This algorithm was implemented with a TMS320C31 DSP. Experiment results prove that the observer is able to estimate the speed and position with a little errors.

• 제어로봇시스템학회:학술대회논문집
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• 2000.10a
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• pp.230-230
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• 2000

This paper describes a model reference adaptive system(MRAS) for speed control of vector-controlled induction motor without a speed sensor. The proposed approach is based on observing the instantaneous torque. The real torque is calculated by sensing stator current and estimated torque is calculated by stator current that is calculated by using estimated rotor speed. The speed estimation error is linearly proportional to error between real torque and estimated torque. The proposed feedback loop has linear component. Furthermore proposed method is robust to parameters variation. The effectiveness is verified by equation and simulation

• 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.

• Proceedings of the KIEE Conference
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• 1996.07a
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• pp.458-460
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• 1996

In this study, the full-state flux observer is designed in the synchronous DQ-frame and the speed adaptation rule is derived by using the MRAS(Model Reference Adaptive System) theory. In this rule, the induction motor becomes a reference model and the flux observer is taken as a adjustable model. A guideline of the adaptation gain is investigated for the precise and stable speed adaptation and the proposed scheme is compared with the conventional one designed in the stationary DQ-frame.

• Journal of the Korean Institute of Telematics and Electronics
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• v.21 no.4
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• pp.78-83
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• 1984

A method is proposed for the adaptive control of linear time varying discrete systems. The stability problem of the model reference adaptive control systems is considered by means of the properties of hypergtability, The hyperstability approach also allows for solutions to the adaptation mechanism. Employing the principles of the continuous time case with output feedback renders it to the discrete case which simplified the system design. The system response is obtained by applying the ramp and step input as a reference signal to the system respectively. With checking the adaptation time for ramp and step input the validity of proposed algorithm was confirmed by the computer simulation.

• The Proceedings of the Korean Institute of Illuminating and Electrical Installation Engineers
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• v.9 no.6
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• pp.71-77
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• 1995

This paper deals with design method of learning fuzzy controller for control of an unknown nonlinear plant using the self-tuning algorithm of fuzzy inference rules. In this method the fuzzy identification model obtained that the joined identification model of nonlinear part and linear identification model of linear part by fuzzy inference systems. This fuzzy identification model ordered self-tuning by Decent method so as to be servile to nonlinear plant. A the end, designed learning fuzzy controller of fuzzy identification model have learning structure to model reference adaptive system. The simulation results show that th suggested identification and learning control schemes are practically feasible and effective.

• Proceedings of the KIEE Conference
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• 1995.07b
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• pp.714-716
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• 1995

This Paper presents a novel and systematic approach to a self-learning controller. The proposed controller is built on a neural network consisting of a standard back propagation (BNN) and approxinate reasoning (AR). The fuzzy inference and knowledge representation are carried out by the neural network structure and computing, instead of logic inference. An architecture similar to that used by traditional model reference adaptive control system (MRAC) is employed.