• 제어로봇시스템학회:학술대회논문집
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• 2000.10a
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• pp.349-349
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• 2000

A Clustering Adaptive Fuzzy Logic Controller(CAFLC) is applied to the torque control of a brushless do motor drive. Objective of this system includes elimination of torque ripple due to cogging at low speeds under loads. The CAFLC implemented has advantages of computational simplicity, and self-tuning characteristics. Simulation results showed that the torque ripple and dynamic response of the system using a CAFLC were superior to the model reference adaptive controlled system.

• The Transactions of the Korean Institute of Electrical Engineers
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• v.36 no.10
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• pp.748-754
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• 1987

A robustness analysis for model reference adaptive control(MRAC) system with plant uncertainty is discussed in this paper. The adaptive control system is designed under assumptions that the controlled plant is represented by a lst order nominal model and that the system is drived by a constant reference signal. When using general gradient method(GGM), it is shown that unmodelled dynamics in plant model can cause the instability of the overall control loop during the adaptation process. However, as the algorithm of least square method(LSM) is introduced, the global stability of the system can be hold. And it is also given that the boundedness of adjustable parameters may be verified using the concept of an equilibrium point analysis.

• 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.

• 제어로봇시스템학회:학술대회논문집
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• 1988.10a
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• pp.289-294
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• 1988

This paper describes tbe design and implementation of the adaptive controller to maintain the glood weld quality in gas metal arc welding process. The weld torch travel speed and the surface temperature are taken, respectively, as an input and an output of the welding control system. Because of the very complex phenomena of the process, the input-output dynamic model was experimentally identified by AIC (Akiake Information Criterion). Based on the model structure, the explicit model reference adaptive controller is simulated in order to regulate the output tempernture to the desired level.

• 제어로봇시스템학회:학술대회논문집
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• 1987.10b
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• pp.193-197
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• 1987

Decentralized model reference adaptive controller is used to control interconnected system. Influences caused by interactions between each subsystem are regarded as unmodeled dynamics or disturbances, thus decentralized adaptive controller is designed using MRAC algorithms which guarantees robustness. To expand the stability regions of over all system and to improve control performances, higher level controller is introduced to adjust the control factors such as filter band, size of deadzone or maximum norm of parameter. Local controllers for each subsystem are realized in real time and higher level controller has an ability of detecting the instability phenomena and adjusts the local controller by analysis of power spectrum or square sum of tracking errors.

• Proceedings of the KIPE Conference
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• 1998.10a
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• pp.11-16
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• 1998

In this paper, simultaneous estimation of rotor speed and time constant for a voltage source inverter (VSI) fed induction motor drive are disccussed. The theory is based on the Model Reference Adaptive System (MRAS). The identifier executes Simultaneous rotor speed and time constant so that vector control of the induction may be achieved in the rotor-flux oriented reference frame. Furthermore, to eliminate the offset error caused by the change in the stator resistance, a fuzzy resistance regulator is also designed which operates in parallel with the rotor speed and time constant identifier

• 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.

• International Journal of Aeronautical and Space Sciences
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• v.18 no.2
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• pp.290-299
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• 2017

Reconfigurable flight control using various kinds of adaptive control methods has been studied since the 1970s to enhance the survivability of aircraft in case of severe in-flight failure. Early studies were mainly focused on the failure of actuators. Recently, studies of reconfigurable flight controls that can accommodate complex damage (partial wing and tail loss) in conventional aircraft were reported. However, the partial wing loss effects on the aerodynamics of conventional type aircraft are quite different to those of BWB(blended wing body) aircraft. In this paper, a reconfigurable flight control algorithm was designed using a direct model reference adaptive method to overcome the instability caused by a complex damage of a BWB aircraft. A model reference adaptive control was incorporated into the inner loop rate control system enhancing the performance of the baseline control to cope with abrupt loss of stability. Gains of the model reference adaptive control were polled out using the Liapunov's stability theorem. Outer loop attitude autopilot was designed to manage roll and pitch of the BWB UAV as well. A 6-DOF dynamic model was built-up, where the normal flight can be made to switch to the damaged state abruptly reflecting the possible real flight situation. 22% of right wing loss as well as 25% loss for both vertical tail and rudder control surface were considered in this study. Static aerodynamic coefficients were obtained via wind tunnel test. Numerical simulations were conducted to demonstrate the performance of the reconfigurable flight control system.

• Journal of Institute of Control, Robotics and Systems
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• v.4 no.2
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• pp.203-208
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• 1998

This paper presents an adaptive fuzzy controller using an neural network and adaptation algorithm. Reference-model following neuro-fuzzy controller(RMFNFC) is invesgated in order to overcome the difficulty of rule selecting and defects of the membership function in the general fuzzy logic controller(FLC). RMFNFC is developed to tune various parameter of the fuzzy controller which is used for the discrete nonlinear system control. RMFNFC is trained with the identification information and control closed loop error. A closed loop error is used for design criteria of a fuzzy controller which characterizes and quantize the control performance required in the overall control system. A control system is trained up the controller with the variation of the system obtained from the identifier and closed loop error. Numerical examples are presented to control of the discrete nonlinear system. Simulation results show the effectiveness of the proposed controller.

• Journal of Institute of Control, Robotics and Systems
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• v.8 no.9
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• pp.728-735
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• 2002

The Field robot means the machinery applied for outdoor tasks in construction, agriculture and undersea etc. In this study, to field-robotize a hydraulic excavator that is mostly used in construction working, we have developed an automatic excavation system and an adaptive control system. A model-reference adaptive controller has been designed based on the model that is obtained through off-line system identification. It is illustrated by computer simulations that the proposed control system gives good performance in the trajectory tracking control and the adaptation to parameter variation.