• Proceedings of the KIEE Conference
• /
• 1995.07b
• /
• pp.947-949
• /
• 1995

In this paper, model reference adaptive fuzzy controller (MRAFC) was proposed in order to overcome the difficulty of extracting rules and defects of the adaptation performance in the FLC. MRAFC comprised inner feedback loop consisting of the FLC and plant, and outer loop consisting of an adaptation mechanism which is designed for tuning a control rule of the FLC. A reference-model was used for design criteria of a fuzzy controller which characterizes and quantizes the control performance required in the overall control system. Tuning control rules of FLC is performed by the adaptation mechanism. For this, the fuzzy model for tuning the contorl rules is designed in accordance with the feature of error information. And DC servo motor was selected for case study of actual industrial plant and tested on various loads.

• The Transactions of the Korean Institute of Electrical Engineers D
• /
• v.53 no.6
• /
• pp.389-394
• /
• 2004

This paper proposes a speed estimation method using FLC(Fuzzy Logic Controller) in order to realize the speed control of PMAM(Permanent Magnet AC Motor) with no speed sensor. This method uses FLC as a adaptive laws of MRAS(Model Reference Adaptive System) and estimates the rotor speed of PMAM with a difference between the reference model and the adjustable model. Speed control is performed by PI controller with the estimated speed. The experiment is executed to verify the propriety and the effectiveness of the proposed system.

• Journal of the Semiconductor & Display Technology
• /
• v.23 no.2
• /
• pp.92-99
• /
• 2024

As semiconductor processes require several nanometers precision, the importance of motor control is increasing in semiconductor equipment. Due to unpredictable uncertainties such as friction and mechanical vibrations achieving precise position control in semiconductor processes is challenging. The internal model principle-based controller is a control technique that ensures robust steady-state performance by incorporating a model of the reference and disturbance. The disturbance observer-based controller is a prominent robust control technique implemented to cope with various nonlinearities and uncertainties. Provided that the two controllers can be designed to exhibit equivalent performance under certain conditions, this paper demonstrates through experiments that they yield identical results for the case of a BLDC position control problem. The experimental results also indicate that they can offer enhanced robustness compared with the conventional PID controller in the presence of a time-varying disturbance.

• International Journal of Aeronautical and Space Sciences
• /
• v.11 no.3
• /
• pp.240-245
• /
• 2010

The controller of a model reference adaptive control monitors the plant's inputs and outputs to acknowledge its characteristics. It then adapts itself to the characteristics it encounters instead of behaving in a fixed manner. An important part of every adaptive scheme is the adaptive law for estimating the unknown parameters on line. A more precise model is required to improve performance and to stabilize a given dynamic system, such as a satellite in which performance varies over time and the coefficients change due to disturbances, etc. After model identification, the robust controller ($H{\infty}$) is designed to stabilize the rigid body and flexible body of a satellite, which can be perturbed due to disturbance. The result obtained by the $H{\infty}$ controller is compared with that of the proportional and integration controller which is commonly used for stabilizing a satellite.

• Journal of Power Electronics
• /
• v.14 no.3
• /
• pp.549-563
• /
• 2014

In this paper, the speed regulation problem of permanent synchronous motor (PMSM) systems under the vector control framework is studied. A model reference adaptive controller (MRAC) based on the Lyapunov stability theory is first designed. Since the standard MRAC method provides poor disturbance rejection performance in the case of strong disturbances, a composite control method which combines the MRAC method and the disturbance estimation method, called the MRAC+ESO method, is proposed. An extended state observer (ESO) is introduced to estimate the lumped disturbances. The obtained estimated value acts as a feedforward compensation term to the MRAC controller. A stability analysis of the composite control method is given. Simulation and experimental results are presented and compared to show the effectiveness of the proposed control method.

• The Transactions of the Korean Institute of Electrical Engineers D
• /
• v.55 no.4
• /
• pp.154-157
• /
• 2006

This paper is proposed 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.

• Journal of the Institute of Electronics Engineers of Korea SC
• /
• v.41 no.3
• /
• pp.39-46
• /
• 2004

This paper is proposed adaptive fuzzy-neural network(FNN) controller for the speed control of interior permanent magnet synchronous motor(IPMSM) drive. The design of this algorithm based on FNN controller that is implemented by 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 among 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 analysis prove that the proposed control system has strongly high performance and robustness in parameter variation, steady-state accuracy and transient response.

• Journal of Institute of Control, Robotics and Systems
• /
• v.6 no.2
• /
• pp.127-137
• /
• 2000

This paper presents a novel neural based controller which controls the water level of the nuclear power plant steam generator. The controller consists of a model reference feedback linearization controller and a PI controller for stabilizing the feedback linearization controller. The feedback linearization controller consists of a neural network model and an inversing module which uses the neural network model for computing the control input to the steam generator. We chose Piecewise Linearly Trained Network(PLTN) and Recurrent Neural Netwrok(RNN) for an approximator of the plant and used these approximators in calculating the input from the feedback linearization controller. Combining the above two controllers gives a result of better performance than the case which uses only a PI controller Each control result of PLTN and RNN is given.

• The Transactions of The Korean Institute of Electrical Engineers
• /
• v.62 no.10
• /
• pp.1487-1493
• /
• 2013

A new simple control method for active power filter, which can realize the complete compensation of harmonics is proposed. In the proposed scheme, a model-based digital current control strategy is presented. The proposed control system is designed and implemented in a form referred to as internal model control structure. This method provides a convenient way for parameterizing the controller in term of the nominal system model, including time-delays. As a result, the resulting controller parameters are directly set based on the power circuit parameters, which make tuning of the controllers straightforward task. In the proposed control algorithm, overshoots and oscillations due to the computation time delay is prevented by explicit incorporating of the delay in the controller transfer function. In addition, a new compensating current reference generator employing resonance model implemented by a DSP(Digital Signal Processor) is introduced. Resonance model has an infinite gain at resonant frequency, and it exhibits a band-pass filter. Consequently, the difference between the instantaneous load current and the output of this model is the current reference signal for the harmonic compensation.

• Journal of Advanced Marine Engineering and Technology
• /
• v.19 no.3
• /
• pp.77-83
• /
• 1995

Recently, in the field of industrial servo-systems, several methods have been proposed for tracking the reference input fastly and finely without overshoot. These methods, however, are established under hypothesis that structure and parameters of the plant are known accurately and they are time invariant. In practice, it is difficult to obtain the values of plant's parameters accurately and usually plants change with time and operation conditions. In this paper a method to construct the nominal model tracking adaptive control system is proposed. The system is composed of the nomial model which produces a ideal response and the model tracking system with the fuzzy adaptive controller. If the actual plant is equal to the controlled object in the nominal model, the output of the plant is the same as that of the nominal model and the fuzzy adaptive controller becomes idle. However, when the plant changes, the fuzzy adaptive controller of the tracking system operates in order for the output of the plant to track the ideal response. Through the computer simulations under various conditions, it is confirmed that the proposed model tracking system is very effective.