• The Transactions of the Korean Institute of Electrical Engineers A
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• v.49 no.3
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• pp.95-106
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• 2000

The load frequency control of power system is one of important subjects in view of system operation and control. That is even though the rapid load disturbances were applied to the given power system, the stable and reliable power should be supplied to the users, converging unconditionally and rapidly the frequency deviations and the tie-line power flow one on each area into allowable boundary limits. Nonetheless of such needs, if the internal parameter perturbation and the sudden load variation were given, the unstable phenomenal of power system can be often brought out because of the large frequency deviation and the unsuppressible power line one. Therefore, it is desirable to design the robust neuro-fuzzy controller which can stabilize effectively the given power system as soon as possible. In this paper the robust neuro-fuzzy controller was proposed and applied to control of load frequency over multi-area power system. The architecture and algorithm of a designed NFC(Neuro-Fuzzy Controller) were consist of fuzzy controller and neural network for auto tuning of fuzzy controller. The adaptively learned antecedent and consequent parameters of membership functions in fuzzy controller were acquired from the steepest gradient method for error-back propagation algorithm. The performances of the resultant NFC, that is, the steady-state deviations of frequency and tie-line power flow and the related dynamics, were investigated and analyzed in detail by being applied to the load frequency control of multi-area power system, when the perturbations of predetermined internal parameters. Through the simulation results tried variously in this paper for disturbances of internal parameters and external stepwise load stepwise load changes, the superiorities of the proposed NFC in robustness and adaptive rapidity to the conventional controllers were proved.

• Journal of Korean Society of Steel Construction
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• v.21 no.6
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• pp.609-618
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• 2009

This paper proposes a versatile formula which can be used to evaluate the fire resistant time of steel beams under various design conditions. Towards this end, the key parameters which affect the fire performance of steel beams were first determined through thermo-mechanical considerations, and classified into two groups: structural parameters and thermal parameters. Then the degree of influence of each parameter on the fire performance was investigated through a fully coupled thermo-mechanical analysis up to the occurrence of run-away deflection. The accuracy of the numerical model used was verified using an available full-scale fire test before conducting an extensive parametric analysis. Multiple linear regression analysis was performed to obtain the formula which can be used to predict the fire resistance time of steel beams under various design conditions. The statistical analysis showed that the proposed formula is very robust. The application of the formula in practical fire design under the current code was illustrated in detail. The economy and other advantages of the proposed formula were clearly shown.

• Journal of Navigation and Port Research
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• v.32 no.6
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• pp.459-464
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• 2008

In this paper, we present the model-based fuzzy controller for container cranes which effectively performs set-point tracking control of trolley and anti-swaying control under system parameter and disturbance changes. The first part of this paper focuses on the development of Takagi-Sugeno (T-S) fuzzy modeling in a nonlinear container crane system. Parameters of the membership functions are adjusted by a RCGA to have same dynamic characteristics with nonlinear model of a container crane. In the second part, we present a design methodology of the model-based fuzzy controller. Sub-controllers are designed using LQ control theory for each subsystem in fuzzy model and then the proposed controller is performed with the combination of these sub-controllers by fuzzy IF-THEN rules. In the results of simulation, the fuzzy model showed almost similar dynamic characteristics compared to the outputs of the nonlinear container crane model. Also, the model-based fuzzy controller showed not only the fast settling time for the change in parameter and disturbance, but also stable and robust control performances without any steady-state error.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.12 no.4
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• pp.1674-1692
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• 2018

An RFID network is a network in which a reader inquire about the identities of tags and tags respond with their identities to a reader. The diversity of RFID networks has brought about many applications including an inexpensive system where a single reader supports a small number of tags. Such a system needs a tag cognizance scheme that is able to arbitrate among contending tags as well as is simple enough. In this paper, confining our attention to a clan of simple schemes, we propose a randomized scheme with aiming at enhancing the tag cognizance rate than a conventional scheme. Then, we derive an exact expression for the cognizance rate attained by the randomized scheme. Unfortunately, the exact expression is not so tractable as to optimize the randomized scheme. As an alternative way, we develop an upper bound on the tag cognizance rate. In a closed form, we then obtain a nearly optimal value for a key design parameter, which maximizes the upper bound. Numerical examples confirm that the randomized scheme is able to dominate the conventional scheme in cognizance rate by employing a nearly optimal value. Furthermore, they reveal that the randomized scheme is robust to the fallacy that the reader believes or guesses a wrong number of neighboring tags.

• Journal of Institute of Control, Robotics and Systems
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• v.20 no.12
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• pp.1238-1245
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• 2014

This paper proposes an RBFN-based adaptive tracking controller for an electrically driven mobile robot with parametric uncertainties and external disturbances. A mobile robot model considered in this paper includes all models of the robot body and actuators with uncertain kinematic and dynamic parameters, and uncertain frictions and external disturbances. The proposed controller consists of an RBFN(Radial Basis Function Network) and a robust adaptive controller. The presented RBFN is used to approximate unknown nonlinear robot dynamic functions. The proposed controller is adjusted by the adaptation laws obtained through the Lyapunov stability analysis. The proposed control scheme does not a priori need the accurate knowledge of all parameters in the robot kinematics, robot dynamics and actuator dynamics. Also, nominal parameter values are not required in the controller. The global stability of the closed-loop robot control system is guaranteed using the Lyapunov stability theory. Simulation results show the validity and robustness of the proposed control scheme.

• Journal of the Korean Society for Precision Engineering
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• v.18 no.7
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• pp.53-64
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• 2001

The gain-scheduling control technique is vary useful in the control problem incorporating time varying parameters which can be measured in real time. Based on these facts, in this paper the sway control problem of the pendulum motion of a container hanging on the trolly, which transports containers from a container ship to trucks, is considered. In the container crane control problem, suppressing the residual swing motion of the container at the end of acceleration, deceleration or the case of that the unexpected disturbance input exists is main issue. For this problem, in general, the trolley motion control strategy is introduced and applied. But, in this paper, we introduce and synthesize a new type of swing motion control system. In this control system, a small auxiliary mass is installed on the spreader. And the actuator reacts against the auxiliary mass, applying inertial control forces to the container to reduce the swing motion in the desired manner. In this paper, we assume that an plant parameter is varying and apply the gain-scheduling control technique design the anti-swing motion control system for the controlled plant. In this control system, the controller dynamics are adjusted in real-time according to time-varying plant parameters. And the simulation result shows that the proposed control strategy is shown to be useful to the case of time-varying system and, robust to disturbances like winds and initial sway motion.

• Journal of Institute of Control, Robotics and Systems
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• v.2 no.4
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• pp.306-311
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• 1996

This paper discusses the maintenance of the water level of steam generators at its programmed value. The process, the water level of a steam generator, has the nonminimum phase property. So, it causes a reverse dynamics called a swell and shrink phenomenon. This phenomenon is severe in a low power condition below 15 %, in turn makes the start-up of the power plant too difficult. The control algorithm used here incorporates a pole-assignment scheme into the minimum variance strategy and we use a parallel adaptation algorithm for the parameter estimation, which is robust to noises. As a result, the total control system can keep the water level constant during full power by locating closed-loop poles appropriately, although the process has the characteristics of high complexity and nonlinearity. Also, the extra perturbation signals are added to the input signal such that the control system guarantee persistently exciting. In order to confirm the control performance of a proposed pole-assignment self-tuning controller we perform a computer simulation in full power range.

• 한국태양에너지학회:학술대회논문집
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• 2008.04a
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• pp.61-67
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• 2008

In this paper proposed the solar tracking system to use a fuzzy based on PC in order to increase an output of the PV array. The solar tracking system operated two DC motors driving by signal of photo sensor. The control of dual axes is not an easy task due to nonlinear dynamics and unavailability of the parameters. Recently, artificial intelligent control of the fuzzy control, neural-network and genetic algorithm etc. have been studied. The fuzzy control made a nonlinear dynamics to well perform and had a robust and highly efficient characteristic about a parameter variable as well as a nonlinear characteristic. Hence the fuzzy control was used to perform the tracking system after comparing with error values of setting-up. nonlinear altitude and azimuth. In this paper designed a fuzzy controller for improving output of PV array and evaluated comparison with efficient of conventional PI controller. The data which were obtained by experiment were able to show a validity of the proposed controller.

• Journal of Korean Society for Quality Management
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• v.39 no.4
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• pp.507-515
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• 2011

MIL-HDBK-217 has played a pivotal role in reliability prediction of electronic equipments for more than 30 years. Recently, RIAC developed a new methodology $217Plus^{TM}$which officially replaces MIL-HDBK-217. Sensitivity analysis of the 217Plus component models to various parameters has been performed and meaningful observations have been drawn in this study. We first briefly reviewed the $217Plus^{TM}$ methodolog and compared it with the conventional model, MIL-HDBK-217. We then performed sensitivity analysis $217Plus^{TM}$ component models to various parameters. Based on the six parameters and an orthogonal array selected, we have performed indepth analyses concerning parameter effects on the model. Our result indicates that, among various parameters, operating temperature and temperature rise during operation have the most significant impacts on the life of a component, and thus a design robust to high temperature is the most importantly required. Next, year of manufacture, duty cycle, and voltage stress are weaker but may be significant when they are in heavy load conditions. Although our study is restricted to a specific type of diodes, the results are still valid to other cases. The results in this study not only figure out the behavior of the predicted failure rate as a function of parameters but provide meaningful guidelines for practical applications.

• Journal of Electrical Engineering and Technology
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• v.8 no.3
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• pp.402-410
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• 2013

In this paper, a novel and robust Power System Stabilizer (PSS) is proposed as an effective approach to improve stability in electric power systems. The dynamic performance of proposed PSS has been thoroughly compared with Conventional PSS (CPSS). Both the Real Coded Genetic Algorithm (RCGA) and Particle Swarm Optimization (PSO) techniques are applied to optimum tune the parameter of both the proposed PSS and CPSS in order to damp-out power system oscillations. Due to the high sufficiency of both the RCGA and PSO techniques to solve the very non-linear objective, they have been employed for solution of the optimization problem. In order to verify the dynamic performance of these devices, different conditions of disturbance are taken into account in Single Machine Infinite Bus (SMIB) power system. Moreover, to ensure the robustness of proposed PSS in damping the power system multi-mode oscillations, a Multi Machine (MM) power system under various disturbances are considered as a test system. The results of nonlinear simulation strongly suggest that the proposed PSS significantly enhances the power system dynamic stability in both of the SMIB and MM power system as compared to CPSS.