• Journal of the Korea Academia-Industrial cooperation Society
• /
• v.17 no.3
• /
• pp.473-479
• /
• 2016

This paper describes an accurate and stable current control method of switched reluctance motors(SRMs), which have recently attracted considerable wide attention owing to their favorable features, such as high performance, high durability, structural simplicity, low cost, etc. In most cases, the PI controllers(PICC) have been used mostly for the current control of electric motors because their algorithm and selection of controller gain are relatively simpler compared to other controllers. On the other hand, the PI controller requires an adjustment of the controller gains for each operating point when nonlinear system parameters change rapidly. This paper presents a stable current control method of an SRM using self-tuning fuzzy current controller(STFCC) under nonlinear parameter variation. The performance of the considered method is validated via a dynamic simulation of the current controlled SRM drive using Matlab/Simulink program.

• Proceedings of the KIEE Conference
• /
• 2009.07a
• /
• pp.1080_1081
• /
• 2009

This paper proposes a cooling system using self tuning PI controller for improving the output of BIPV module. The temperature characteristics in regard to improving the output of BIPV system has rarely been studied up to now but some researchers only presented the method using a ventilator. The cooling system efficiency of BIPV module applied to a ventilator mainly depends on the weather such as wind and insolation etc. Because the cooling system of BIPV module using a ventilator is so sensitive, that is being set off by wind speed at all time but is unable to operate in the nominal operating cell temperature(NOCT) which is able to make the maximum output. The paper proposes the cooling system using thermoelectron by self tuning PI controller so as to solve such problems. The thermoelectron control of self tuning PI controller can be controlled independently in the outside environment because that is performed by micro-controller. The temperature control of thermoelectron, also, can be operated around NOCT through algorism of the temperature control. Therefore, outputs of the whole system increase and the efficiency rises. The paper demonstrates the validity of proposed method by comparing the data obtained through a experiment of the cooling method of BIPV using a ventilator and proposed thermoelectron

• Journal of Institute of Control, Robotics and Systems
• /
• v.22 no.11
• /
• pp.925-931
• /
• 2016

The BLDC motor is used widely in industry due to its controllability and freedom from maintenance because there is no mechanical brush in the BLDC motor. Furthermore, it is suitable for high-speed applications, such as compressors and air blowers. For instance, for a compressor with a small impeller due to miniaturizing, the BLDC motor has to rotate at a very high speed to maintain the compression ratio of the compressor. Typically, to reach an ultra-high speed, airfoil bearings must be used in place of ball bearings because of their friction. Unfortunately, the characteristics of airfoil bearings change drastically depending on the revolution speed. In this paper, a BLDC motor with airfoil bearings is controlled with a PID controller. To analyze and determine the PID coefficients, the relay-feedback method is used. Additionally, for adaptive control, a fuzzy logic controller is used. Furthermore, the auto-tuning and self-tuning techniques are combined to control the BLDC motor. The proposed method is able to control the airfoil-bearing BLDC motor efficiently.

• Journal of Mechanical Science and Technology
• /
• v.15 no.5
• /
• pp.580-591
• /
• 2001

This paper proposes a fuzzy-sliding mode control which is designed by a self tuning fuzzy inference method based on a genetic algorithm. Using the method, the number of inference rules and the shape of the membership functions of the proposed fuzzy-sliding mode control are optimized without the aid of an expert in robotics. The fuzzy outputs of the consequent part are updated by the gradient descent method. It is further guaranteed that the selected solution becomes the global optimal solution by optimizing Akaikes information criterion expressing the quality of the inference rules. In order to evaluate the learning performance of the proposed fuzzy-sliding mode control based on a genetic algorithm, a trajectory tracking simulation of the polishing robot is carried out. Simulation results show that the optimal fuzzy inference rules are automatically selected by the genetic algorithm and the trajectory control result is similar to the result of the fuzzy-sliding mode control which is selected through trial error by an expert. Therefore, a designer who does not have expert knowledge of robot systems can design the fuzzy-sliding mode controller using the proposed self tuning fuzzy inference method based on the genetic algorithm.

• Proceedings of the KIEE Conference
• /
• 2004.10a
• /
• pp.173-175
• /
• 2004

This paper presents a novel design of a self tuning PI controller of induction motor using fuzzy control. In this approach, the fuzzy tuning of a PI controller gains is achieved through fuzzy rules deduced from many robustness simulation tests applied to several induction motors, for a variety of operating conditions such as response to speed command from standstill, step load torque application and speed variations, with nominal parameters and an changed rotor resistance, self inductance and inertia. Simulation results on a speed controller of induction motor are presented to show the effectiveness of the proposed gain tuner. And this controller is better than the fixed gains one in terms of robustness, even under great variations of operating conditions and load disturbance.

• Journal of the Korean Institute of Telematics and Electronics
• /
• v.22 no.2
• /
• pp.6-12
• /
• 1985

In this paper, self tuning control algorithm based on least square method is applied to the speed control of D.C. motor using Z-80 microprocessor as control unit. And the performance of algorithm is analyzed when the correlated noises of variance 20 and 80 are applied respectively. The convergence speed is measured and tracking is verified for the step and staircase wave reference input. Also it is shown that self tuning control algorithm is more attractive to the D.C. Totor speed control system regardless of power supply voltage and friction load changes than linear feedback control method which doesn't estimate parameters.

• Journal of the Korean Institute of Intelligent Systems
• /
• v.7 no.2
• /
• pp.52-65
• /
• 1997

This paper presents a design technique of self tuning fuzzy controller for load frequency control of power system. The proposed parameter self tuning algorithm of fuzzy controller is based on the gradient method using four direction vectors which make error between inference values of fuzzy controller and output values of the specially selected optimal controller reduce steepestly. Using input-output data pair obtained from optimal controller, the parameters in antecedent part and in consequent part of fuzzy inference rules are learned and tuned automatically using the proposed gradient method. The related simulation results show that the proposed fuzzy controller is more powerful than the conventional ones for reductions of undershoot and steady-state load frequency deviation and for minimization of settling time.

• Journal of the Institute of Electronics Engineers of Korea SC
• /
• v.41 no.4
• /
• pp.21-28
• /
• 2004

This paper presents a direct multivariable self-tuning controller using neural network which adapts to the changing parameters of the higher order multivariable nonlinear system with nonminimum phase behavior, mutual interactions and time delays. The nonlinearities are assumed to be globally bounded, and a multivariable nonlinear system is divided linear part and nonlinear part. The neural network is used to estimate the controller parameters, and the control output is obtained through estimated controller parameter. In order to demonstrate the effectiveness of the proposed algorithm the computer simulation is done to adapt the multivariable nonlinear nonminimm phase system with time delays and changed system parameter after a constant time. The proposed method compared with direct multivariable adaptive controller using neural network.

• 제어로봇시스템학회:학술대회논문집
• /
• 1990.10a
• /
• pp.663-668
• /
• 1990

This paper deals with the application of a Generalized Predictive Control (CPC) to a Pressurized Water Reactor (P.W.R) Nuclear Power Plant. Generalized Predictive Control is a sort of Explicit Self-Tuning Control. Current self-tuning algorithms lack robustness to prior choices of either dead-time (input time delay of a plant) or model order. GPC is shown by simulation studies to be superior to accepted self-tuning techniques such as minimum variance and pole-placement from the viewpoint that it is robust to prior choices of dead-time or model order. In this paper a GPC controller is designed to control the P.W.R. nuclear power rlant with varying dead-time and through the designing procedure the designer is free from the constraint of knowing the exact dead-time. The controller is constructed based on the 2nd order linear model approximated in the vicinity of operating point. To ensure that this low-order model describes the complex real dynamics well enough for control purposes, model parameters are updated on-line with a Recursive Least Squares algorithm. Simulation results are successful and show the possibilities of the GPC control application to actual plants with varying or unknown dead-time.

• Proceedings of the KIEE Conference
• /
• 2006.07b
• /
• pp.759-760
• /
• 2006

In this paper proposed to Neural network PI self-tuning direct controller using Error back propagation algorithm. Proposed controller applies to speed controller and current controller. Also, this built up the interface environment to drive it simply and exactly in any kind of reference, environment fluent and parameter transaction of BLDC motor. Neural network PI self-tuning simulator using Visual C++ and Matlab Simulation is organized to construct this environment. Built-u-p interface has it's own purpose that even the user who don't have the accurate knowledge of neural network can embody operation characteristic rapidly and easily.