• Proceedings of the KIEE Conference
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• 2000.07d
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• pp.2314-2316
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• 2000

In the construction of successful fuzzy models and/or controllers for nonlinear systems, identification of a good fuzzy Neural inference system is an important yet difficult problem, which is traditionally accomplished by trial and error process. In this paper, we propose a systematic identification procedure for complex multi-input single- output nonlinear systems with DNA coding method.DNA coding method is optimization algorithm based on biological DNA as are conventional genetic algothms (GAs). We also propose a new coding method for applying the DNA coding method to the identification of fuzzy Neural models. To acquire optimal TS fuzzy model with higher accuracy and economical size, we use the DNA coding method to optimize the parameters and the number of fuzzy inference system.

• International Journal of Fuzzy Logic and Intelligent Systems
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• v.7 no.3
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• pp.216-220
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• 2007

Camera calibration in machine vision is the process of determining the intrinsic camera parameters and the three-dimensional (3D) position and orientation of the camera frame relative to a certain world coordinate system. On the other hand, Takagi-Sugeno-Kang (TSK) fuzzy system is a very popular fuzzy system and approximates any nonlinear function to arbitrary accuracy with only a small number of fuzzy rules. It demonstrates not only nonlinear behavior but also transparent structure. In this paper, we present a novel and simple technique for camera calibration for machine vision using TSK fuzzy model. The proposed method divides the world into some regions according to camera view and uses the clustered 3D geometric knowledge. TSK fuzzy system is employed to estimate the camera parameters by combining partial information into complete 3D information. The experiments are performed to verify the proposed camera calibration.

• The Journal of Korea Robotics Society
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• v.19 no.1
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• pp.8-15
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• 2024

This paper proposes a linear model predictive control of 6-DOF remotely operated underwater vehicles using nonlinear robust internal-loop compensator (NRIC). First, we design a integrator embedded linear model prediction controller for a linear nominal model, and then let the real model follow the values calculated through forward dynamics. This work is carried out through an NRIC and in this process, modeling errors and external disturbance are compensated. This concept is similar to disturbance observer-based control, but it has the difference that H_∞ optimality is guaranteed. Finally, tracking results at trajectory containing the velocity discontinuity point and the position tracking performance in the disturbance environment is confirmed through the comparative study with a traditional inverse dynamics PD controller.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.23 no.61
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• pp.41-50
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• 2000

Cell formation(CF) Is to group parts with similar geometry, function, material and process into part families, and the corresponding machines into machine cells. Cell formation solutions often contain exceptional elements(EEs). Also, the following objective functions - minimizing the total costs of dealing with exceptional elements and maximizing total similarity coefficients between parts - have been used in CF modeling. Thus, multiobjective programming approach can be developed to model cell formation problems with two conflicting objective functions. This paper presents an effective cell formation method with fuzzy multiobjective nonlinear mixed-integer programming simultaneously to form machine cells and to minimize the cost of eliminating EEs.

• Journal of Mechanical Science and Technology
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• v.14 no.5
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• pp.508-517
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• 2000

For shift quality improvement, torque sensors are currently too expensive to be used on production vehicles. To achieve smooth acceleration shift, the reference trajectory of the clutch slip speed for accomplishing the shift process within a designated shift completion time and its relationship with the clutch actuating torque were suggested by Jeong and Lee (1999). In order to facilitate the proposed algorithm, nonlinear estimators for necessary information such as the axle shaft torque, clutch friction and turbine torque were designed using only speed sensors. Accounting for the modeling error, a control law for this indirect smooth shift was proposed based on the above mentioned suggestions. Simulation results of the proposed estimators and shift controller were presented and further considerations for practical applications are discussed.

• Journal of the Korean Institute of Intelligent Systems
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• v.10 no.5
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• pp.432-441
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• 2000

This paper deals with the fuzzy modeling for the complex and uncertain nonlinear systems, in which conventional and mathematical models may fail to give satisfactory results. Genetic algorithm has been used to identifY parameters and structure of fuzzy model because it has the ability to search optimal solution somewhat globally. The genetic algorithm, however, has a problem, which optimization process can be premature convergence in the case of lack of genetic divergence of population. Virus- evolutionary genetic algorithm(VEGA) could be a strategy against this local convergence. Therefore, we use VEGA for fuzzy modeling. In this method, local information is exchanged in population so that population can sustain genetic divergence. finally, to prove the theoretical hypothesis, we provide numerical examples to evaluate the feasibility and generality of fuzzy modeling using VEGA.

• The Transactions of the Korean Institute of Electrical Engineers D
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• v.50 no.7
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• pp.339-349
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• 2001

This paper suggests an optimal identification method for complex and nonlinear system modeling that is based on Fuzzy-Neural Networks(FNN). The proposed Hybrid Identification Algorithm is based on Yamakawa's FNN and uses the simplified inference as fuzzy inference method and Error Back Propagation Algorithm as learning rule. In this paper, the FNN modeling implements parameter identification using HCM algorithm and hybrid structure combined with two types of optimization theories for nonlinear systems. We use a HCM(Hard C-Means) clustering algorithm to find initial apexes of membership function. The parameters such as apexes of membership functions, learning rates, and momentum coefficients are adjusted using hybrid algorithm. The proposed hybrid identification algorithm is carried out using both a genetic algorithm and the improved complex method. Also, an aggregated objective function(performance index) with weighting factor is introduced to achieve a sound balance between approximation and generalization abilities of the model. According to the selection and adjustment of a weighting factor of an aggregate objective function which depends on the number of data and a certain degree of nonlinearity(distribution of I/O data), we show that it is available and effective to design an optimal FNN model structure with mutual balance and dependency between approximation and generalization abilities. To evaluate the performance of the proposed model, we use the time series data for gas furnace, the data of sewage treatment process and traffic route choice process.

• Proceedings of the Korean Society for Noise and Vibration Engineering Conference
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• 2000.06a
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• pp.461-467
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• 2000

Mechanical problems are basically three dimensional nonlinear dynamic problems, which makes it difficult to solve. The difficulties are tried to overcome by modeling, i.e., simplifications of the system with the assumptions or negligence of minute effects. However, the correctness or usefulness of the model should be verified through the comparison with experimental results, which is the process of physical understanding of the system. The understanding of physics of the system make it possible to design or operation of the system. The effects of clearance and friction are always difficult problems in mechanical system due to its nonlinearity. The nonlinearity comes from piecewise-linear characteristics of the stiffness and damping of the system. The modeling of piecewise-linearity and the experimental result are discussed in this paper for impact and friction oscillator and rotor rubbing problem, which is the combination of impact and friction problems.

• Journal of Power Electronics
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• v.18 no.6
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• pp.1901-1911
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• 2018

This study presents a circuit model for simulating the switching transients of insulated-gate bipolar transistors (IGBTs) with inductive load switching. The modeling approach used in this study considers the behavior of IGBTs and freewheeling diodes during the transient process and ignores the complex semiconductor physics-based relationships and parameters. The proposed circuit model can accurately simulate the switching behavior due to the detailed consideration of device-circuit interactions and the nonlinear nature of model parameters, such as internal capacitances. The developed model is incorporated in an IGBT loss calculation module of an electromagnetic transient simulation program to enable the estimation of switching losses in voltage source converters embedded in large power systems.

• Transactions of the Korean Society for Noise and Vibration Engineering
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• v.19 no.10
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• pp.1034-1042
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• 2009

In this paper, the magic formula model was applied for a friction damper in a drum-type washing machine. To describe characteristics of the hysteretic damping force, Physical tests were first carried out to get experimental results using an MTS machine. Then, parameters for the magic formula model were determined from the experimental curves. The ADAMS and MATLAB programs were used for the multibody modeling of the damper and process for parameter identification. The model of drum-type washing machine was applied for a dynamic model of friction damper, in which the accuracy of the proposed damper model was verified.