• 제어로봇시스템학회:학술대회논문집
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• 2004.08a
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• pp.1500-1505
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• 2004

This paper presents the design and control of a small Brushless DC (BLDC) Motor for entertainment robots. In order to control the developed BLDC motor, Adaptive Fuzzy Control (AFC) scheme via Parallel distributed Compensation(PDC) is developed for the multi- input/multi-output plant model represented by the Takagi-Sugeno(TS) model. The alternative AFC scheme is proposed to provide asymptotic tracking of a reference signal for the systems with uncertain or slowly time-varying parameters. The developed control law and adaptive law guarantee the boundedness of all signals in the closed-loop system. In addition, the plant state tracks the state of the reference model asymptotically with time for any bounded reference input signal. The suggested design technique is applied to the velocity control of a developed small BLDC motor for entertainment robots.

• Journal of Advanced Marine Engineering and Technology
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• v.36 no.6
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• pp.830-836
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• 2012

Various control algorithms have been proposed for the speed-sensorless control for an induction motor. These control schemes are mainly based on the speed feedback with the flux and speed estimations. This paper proposes another method for the speed-sensorless control for an induction motor. The proposed scheme is based on the torque and flux compensation without speed estimations, in which the same controlled stator voltage is applied to both the induction motor and the numerical model so that the differences between torques and fluxes of the model and the induction motor may be compelled to give access to zero. The results of experiment show the effectiveness of the scheme.

• The Transactions of the Korean Institute of Power Electronics
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• v.21 no.1
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• pp.65-71
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• 2016

This study investigates a high-accuracy alternate QD model to estimate the characteristics of induction motor under light loads. To demonstrate the usefulness of the alternate QD model, a maximum torque per amp (MTPA) control based on the alternate model is shown to outperform MTPA control based on the standard QD model. The experimental study conducted in this work exhibits that the MTPA control based on the alternate QD model tracks torque commands between 20 Nm and 30 Nm with 5% error, whereas the MTPA control based on the standard QD model generates torques lower by over 23% compared with the aforementioned torque commands. This result indicates that the alternate QD model is a highly accurate model for induction motors under light loads.

• Transactions on Control, Automation and Systems Engineering
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• v.2 no.3
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• pp.207-213
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• 2000

This paper introduces a high-performance speed control system based on artificial neural networks(ANN) to estimate unknown parameters of a DC servo motor. The goal of this research is to keep the rotor speed of the DC servo motor to follow an arbitrary selected trajectory. In detail, the aim is to obtain accurate trajectory control of the speed, specially when the motor and load parameters are unknown. By using an artificial neural network, we can acquire unknown nonlinear dynamics of the motor and the load. A trained neural network identifier combined with a reference model can be used to achieve the trajectory control. The performance of the identification and the control algorithm are evaluated through the simulation and experiment of nonlinear dynamics of the motor and the load using a typical DC servo motor model.

• Proceedings of the KIEE Conference
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• 2002.07d
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• pp.2780-2782
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• 2002

In this paper Valentine Braitenberg structure based sensory motor model for object tracking control system was proposed. Conventional model based control schemes are require highly non-linear mathematical models, which require long computational time to solve complex high order equations. Contrast to conventional models proposed system simply link signal data from camera directly to the inputs of neural network, and outputs of network are directly fed into input of motor driver of camera. With simple structure of sensory motor model, real time tracking control system for dynamic object was realized successfully, and the implementation of sensory motor model can overcome the limitation of model-based control schemes.

• The Transactions of the Korean Institute of Power Electronics
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• v.7 no.3
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• pp.215-223
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• 2002

This paper investigates the adaptive control of a fuzzy logic based speed and flux controller fur a vector controlled induction motor drive. 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 model reference adaptive control(MRAC) fuzzy controller is evaluated by simulation for various operating conditions. The validity of the Proposed MRAC fuzzy controller is confirmed by performance results for induction motor drive system.

• Journal of Electrical Engineering and Technology
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• v.11 no.2
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• pp.470-479
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• 2016

The model based method for energy saving of the separately excited DC motor drive system is proposed in the paper. The accurate power loss model is necessary for this method. Therefore, the adaptive tabu search algorithm is applied to identify the parameters in the power loss model. The field current values for minimum power losses at any load torques and speeds are calculated by the proposed method. The rule based controller is used to control the field current and speed of the motor. The experimental results confirm that the model based method can successfully provide the energy saving for separately excited DC motor drive. The maximum value of the energy saving is 48.61% compared with the conventional drive method.

• Journal of Power Electronics
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• v.12 no.2
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• pp.305-316
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• 2012

The paper presents an accurate loss model based controller of an induction motor to calculate the optimal air gap flux. The model includes copper losses, iron losses, harmonic losses, friction and windage losses, and stray losses. These losses are represented as a function of the air gap flux. By using the calculated optimal air gap flux compared with rated flux for speed sensorless indirect vector controlled induction motor, an improvement in motor efficiency is achieved. The motor speed performance is improved using a fuzzy logic speed controller instead of a PI controller. The fuzzy logic speed controller was simulated using the fuzzy control interface block of MATLAB/SIMULINK program. The control algorithm is experimentally tested within a PC under RTAI-Linux. The simulation and experimental results show the improvement in motor efficiency and speed performance.

• Journal of Institute of Control, Robotics and Systems
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• v.18 no.7
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• pp.627-631
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• 2012

Observable mathematical model of DDM (Direct Dirve Motor) was suggested. The motor that operates the object system directly is called DDM. DDM has many strong points, however, it has a significant disadvantage, that it is more sensitive to the external force than the motor with reduction gear. In other word, if the force is applied, air gap of the motor can be perturbed. This causes not only difficulty in motor control but also even more serious problem, such as the breakdown of motor. However, if the air gap variation can be estimated, it can help prevent these problems. DDM should be modeled to estimate the air gap variation. The type of researched DDM is PMSM (Permanent Magnet Synchronous Motor) and precedent model of PMSM includes only characteristics of electro-magnetic system and rotational motion. However, suggested model should also include characteristics of translational motion of rotor to estimate the air gap variation. Also, this model should satisfy observability condition, because state observer is designed based on this model.

• Journal of the Korean Institute of Telematics and Electronics
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• v.18 no.3
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• pp.35-41
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• 1981

In this paper, the application of model reference adaptive control theory to the D.C motor speed control using the microprocessor is studied. It is shown that with the use of an adaptive control algorithm the error between output of the motor and the reference model, which is approximated to first order, can be conve to zero. By computer simulation and the practical implementation with the microprocessor M 6800, can be concluded that the adaptive control system adapts well to the rapid change of the load and reference inputs.