• Journal of the Institute of Electronics Engineers of Korea SC
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• v.40 no.5
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• pp.317-325
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• 2003

The vibration, which often occurred in a two inertia motor system, makes it difficult to achieve a quick response of speed and disturbance rejection. This paper provides an autonomous pole assignment technique for three kinds of speed controllers (I-P, I-PD, and State feedback) using GAs(Genetic Algorithms) for a two-inertia motor system. Firstly, the optimal parameters are chosen using GAs in view of reducing overshoot and settling time, then those are used in computing the gains of each controller. Some simulation results verify the effectiveness of the proposed design. The proposed controller is expected to be the autonomous design way for controlling a two-inertia motor system with flexible shaft.

• Proceedings of the KIEE Conference
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• 2003.11c
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• pp.1018-1021
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• 2003

This dissertation is reference to measure visual information about the configuration of magnetic field automatically and materialize the new magnetic field mapping system for the rapid and clear measure by using of the mediocrity orthogonal robot in the three- dimensional space required the measure of magnetic field concurrently. The measuring sensor is composed to be available for the measure of three-dimensional direction of magnetic field by vertically conjoining each of three hall sensors utilized of the hall effect and installed Gaussmeter, which is devised to receive the sensor result and the robot controller, away from the measuring robot in order to minimize the affection of magnetic field. Also, the controller and Gaussmeter are composed of Use interface, RS-232C and IEEE-488.2 communication. Interface system is written in NI's LabVIEW and composed to be able to set up a measuring area, the measuring number of times, two and three-dimensional graph, the velocity of robot and the magnetic field distribution graph of each element by inputting parameters. The materialized magnetic field mapping system expert the collection of the data easily and the effect of utilizing data.

• Clinics in Orthopedic Surgery
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• v.10 no.4
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• pp.468-478
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• 2018

Background: The restriction of wrist motion results in limited hand function, and the evaluation of the range of wrist motion is related to the evaluation of wrist function. To analyze and compare the wrist motion during four selected tasks, we developed a new desktop motion analysis system using the motion controller for a home video game console. Methods: Eighteen healthy, right-handed subjects performed 15 trials of selective tasks (dart throwing, hammering, circumduction, and winding thread on a reel) with both wrists. The signals of light-emitting diode markers attached to the hand and forearm were detected by the optic receptor in the motion controller. We compared the results between both wrists and between motions with similar motion paths. Results: The parameters (range of motion, offset, coupling, and orientations of the oblique plane) for wrist motion were not significantly different between both wrists, except for radioulnar deviation for hammering and the orientation for thread winding. In each wrist, the ranges for hammering were larger than those for dart throwing. The offsets and the orientations of the oblique plane were not significantly different between circumduction and thread winding. Conclusions: The results for the parameters of dart throwing, hammering, and circumduction of our motion analysis system using the motion controller were considerably similar to those of the previous studies with three-dimensional reconstruction with computed tomography, electrogoniometer, and motion capture system. Therefore, our system may be a cost-effective and simple method for wrist motion analysis.

• 제어로봇시스템학회:학술대회논문집
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• 2005.06a
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• pp.999-1004
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• 2005

An algorithm for a hybrid controller consists of a sliding mode control part and a fuzzy logic part which ar purposely for nonlinear systems. The sliding mode part of the solution is based on "eigenvalue/vector"-type controller is used as the backstepping approach for tracking errors. The fuzzy logic part is a Mamdani fuzzy model. This is designed by applying sliding mode control (SMC) method to the dynamic model. The main objective is to keep the update dynamics in a stable region by used SMC. After that the plant behavior is presented to train procedure of adaptive neuro-fuzzy inference systems (ANFIS). ANFIS architecture is determined and the relevant formulation for the approach is given. Using the error (e) and rate of error (de), occur due to the difference between the desired output value (yd) and the actual output value (y) of the system. A dynamic adaptation law is proposed and proved the particularly chosen form of the adaptation strategy. Subsequently VSC creates a sliding mode in the plant behavior while the parameters of the controller are also in a sliding mode (stable trainer). This study considers the ANFIS structure with first order Sugeno model containing nine rules. Bell shaped membership functions with product inference rule are used at the fuzzification level. Finally the Mamdani fuzzy logic which is depends on adaptive neuro-fuzzy inference systems structure designed. At the transferable stage from ANFIS to Mamdani fuzzy model is adjusted for the membership function of the input value (e, de) and the actual output value (y) of the system could be changed to trapezoidal and triangular functions through tuning the parameters of the membership functions and rules base. These help adjust the contributions of both fuzzy control and variable structure control to the entire control value. The application example, control of a mass-damper system is considered. The simulation has been done using MATLAB. Three cases of the controller will be considered: for backstepping sliding-mode controller, for hybrid controller, and for adaptive backstepping sliding-mode controller. A numerical example is simulated to verify the performances of the proposed control strategy, and the simulation results show that the controller designed is more effective than the adaptive backstepping sliding mode controller.

• Journal of Electrical Engineering and Technology
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• v.12 no.2
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• pp.761-768
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• 2017

This paper deals with the performance analysis of three phase induction motor considering its stator side faults and operating thermal limits. The speed control of induction motor using three phase boost converter operated by a MOSFET switch and a PI controller is demonstrated and presented in this article. IGBTs switches are used for inverter drive mechanism. The experimental result of speed control of induction motor using voltage control technique clearly shows better accuracy than conventional methods of speed control. A three phase 1HP 415V 0.78 kW 4 Pole induction motor is designed using motor solver software. Based on the parameters used in the software thermal analysis of induction motor is done and torque variation with conductor area, efficiency, output curve, losses in different parts of motor has been obtained. Also different types of faults namely under voltage, over voltage, stator imbalanced voltage, turn to turn, locked rotor bar, wrong alignment of rotor bar with respect to stator are studied and fault analysis is performed. Hence comparison is made based upon the results obtained before and after faults.

• Journal of Power Electronics
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• v.16 no.1
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• pp.190-204
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• 2016

An active front end (AFE) is required for a three-phase induction motor (IM) fed by a voltage source inverter (VSI), because of the increasing need to derive quality current from the utility end without sacrificing the power factor (PF). This study investigates a proportional-plus-integral (PI) controller based AFE topology that uses a super-lift converter (SLC). The significance of the proposed SLC, which converts rectified AC supply to geometrically proceed ripple-free DC supply, is explained. Variations in several power quality parameters in the intended IM drive for 0% and 100% loading conditions are demonstrated. A simulation is conducted by using MATLAB/Simulink software, and a prototype is built with a field programmable gate array (FPGA) Spartan-6 processor. Simulation results are correlated with the experimental results obtained from a 0.5 HP IM drive prototype with speed feedback and a voltage/frequency (V/f) control strategy. The proposed AFE topology using SLC is suitable for three-phase IM drives, considering the supply end PF, the DC-link voltage and current, the total harmonic distortion (THD) in supply current, and the speed response of IM.

• Proceedings of the KIEE Conference
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• 2015.07a
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• pp.1050-1051
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• 2015

In industry, there are several different controllers which can be implemented for power conditioning systems (PCS) such as proportional-resonant (PR), predictive deadbeat (PD), or proportional-integral (PI) controller. But there are not any comparison studies about these controllers. To investigate the differences between the three types of the controllers, this paper presents a comparative study of PR, PI, and PD controllers in a photovoltaic (PV) PCS. These controllers are designed mathematically and simulated for the comparative analysis. The PI controller is designed in the rotating reference (dq) frame. The PR and PD controllers are implemented in the natural (abc) reference frame. The PCS is composed of a DC-DC boost converter and a full bridge inverter. The filter of the PCS is an LCL filter including a passive damping resistor. The parameters of PCS are 3 kW, 25 kHz switching frequency and 220 V-60 Hz grid voltage. The comparison results between these controllers for the grid-connected PCS are clearly shown. The simulation results demonstrate the detailed characteristics of each controller for the PV PCS in order to choose the controller for individual target properly.

• Journal of Electrical Engineering and Technology
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• v.12 no.3
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• pp.1027-1037
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• 2017

Penetration of renewable energy sources makes the modern interconnected power systems to have more intelligence and flexibility in the control. Hence, it is essential to maintain the system frequency and tie-line power exchange at nominal values using Load Frequency Control (LFC) for efficient, economic and reliable operation of power systems. In this paper, intelligent tuning of the Proportional Integral Derivative (PID) controller for LFC in an interconnected power system is considered as a main objective. The chosen problem is formulated as an optimization problem and the optimal gain parameters of PID controllers are computed with three innovative swarm intelligent algorithms named Particle Swarm Optimization (PSO), Bacterial Foraging Optimization Algorithm (BFOA) and hybrid Bacterial Foraging Particle Swarm Optimization (BFPSO) and a comparative study is made between them. A new objective function designed with necessary time domain specifications using weighted sum approach is also offered in this report and compared with conventional objective functions. All the simulation results clearly reveal that, the hybrid BFPSO tuned PID controller with proposed objective function has better control performances over other optimization methodologies.

• Journal of the Korean Institute of Intelligent Systems
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• v.8 no.5
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• pp.29-38
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• 1998

This paper proposes the neural network self-tuning PID controller for the load frequency control of 2- areas power system, namely, the prompt convergence of frequency and tie-line power flow deviation. The neural network applied to computer simulation consists of neurons of two inputs, ten hiddens and tliree outputs layer. Neurons of two inputs layer receive the error and its change rate of the system and cutputs layer consists of three neurons for the parameters of the PID controller. The simulation results shows that the proposed neural network self-tuning PID controller is superior to conventional control t~:chniques(Optimal, PID) in dynamic response and control performance.

• 제어로봇시스템학회:학술대회논문집
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• 1996.10a
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• pp.36-39
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• 1996

This paper proposes identification and control algorithm of nonlinear systems and the proposed fuzzy-neural network has following characteristics. The network is roughly divided into premise and consequence. The consequence function is nonlinear function which consists of three parameters and the membership function in the premise contains of two parameters. The parameters in premise and consequence are learned by the extended back-propagation algorithm which has a modified form of the generalized delta rule. Simulation results on the identification show that this method is more effective than that of Narendra [3]. The indirect fuzzy-neural control is made of the fuzzy-neural identification and controller. Result on the indirect fuzzy-neural control shows that the proposed fuzzy-neural network can be efficiently applied to nonlinear systems.