• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 1997.10a
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• pp.449-452
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• 1997

In this paper, we suggest a variable structure controller using the time-varying nonlinear sliding surface instead of the fixed sliding surface, which has been the robustness against parameter variations and extraneous disturbance during the reaching phase. As appling TS fuzzy algorithm to the regulation of the nonlinear sliding surface, the reaching time of the system trajectory is faster than the fixed method . This proposed scheme has better performance than the conventional method in reaching time parameter variation and extraneous disturbance. To demonstrate its performance, the proposed control algorithm is applied to a rotational inverted pendulum.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 1997.10a
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• pp.383-386
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• 1997

In this paper, We proposed a parallel structure of the Neural Network model to nonlinear complex system. Neural Network was used as basic model which has learning ability and high tolerence level. This paper, we used Neural Network which has BP(Error Back Propagation Algorithm) model. But it sometimes has difficulty to append characteristic of input data to nonlinear system. So that, I used HCM(hard c-Means) method of clustering technique to append property of input data. Clustering Algorithms are used extensively not only to organized categorize data, but are also useful for data compression and model construction. Gas furance, a sewage treatment process are used to evaluate the performance of the proposed model and then obtained higher accuracy than other previous medels.

• Proceedings of the Korea Concrete Institute Conference
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• 1998.04a
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• pp.347-352
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• 1998

The purpose of this paper is to present and establish a procedure to predict the prestress forces during the service life of the structure. The statistical approach of this procedure is using the in-situ measurement data of the post-tensioning system to develop a nonlinear regression analysis. The method of least squares is used to fit a certain function a set of data. Use of a nonlinear model is achieved by its logarithmic transformation and sunsequent use of linear-regression theory. The regression analysis result can be used to check the prestress force during the service life so that the remaining prestress force is equal to or exceeds the design requirement. Results from the measurement data of PSC box girder bridge structure were used to demonstrate the procedures.

• Earthquakes and Structures
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• v.7 no.6
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• pp.937-953
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• 2014

A semi-active control platform comprising the mechanical model of magnetorheological (MR) dampers, the bang-bang control law and damage material models is developed, and the simulation method of steel plate shear wall (SPSW) and optimization method for capacity design of MR dampers are proposed. A 15-story steel frame-SPSW structure is analyzed to evaluate the seismic performance of nonlinear semi-active controlled structures with optimal designed MR dampers, results indicate that the control platform and simulation method are stable and fast, and the damage accumulation effects of uncontrolled structure are largely reduced, and the seismic performance of controlled structures has been improved.

• Transactions of the Korean Society of Mechanical Engineers A
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• v.20 no.9
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• pp.2833-2842
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• 1996

This paper is to practice optimal rigidity design by the strain energy density estimation method for static buckling and sizing design sensitivity analysis for dynamic buckling of a nonlinear vehicle frame structure from those results. Using these sizing design sensitivity resutls, an optimization of a nonlinear vehicle frame structure with dynamic buckling constraint is carrried out with the graient projection method.

• 제어로봇시스템학회:학술대회논문집
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• 2000.10a
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• pp.86-86
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• 2000

This paper describes a neural network based fuzzy type PID control scheme. The PID controller is being widely used in industrial applications. however, it is difficult to determine the appropriate PID gains for (he nonlinear system control. In this paper, we re-analyzed the fuzzy controller as conventional PID controller structure, and proposed a neural network based fuzzy type PID controller whose scaling factors were adjusted automatically. The value of initial scaling factors of the proposed controller were determined on the basis of the conventional PID controller parameters tuning methods and then they were adjusted by using neural network control techniques. Proposed controller was simple in structure and computational burden was small so that on-line adaptation was easy to apply to. The result of practical experiment on the magnetic levitation system, which is known to be hard nonlinear, showed the proposed controller's excellent performance.

• The Journal of Korean Institute of Electromagnetic Engineering and Science
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• v.11 no.4
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• pp.667-673
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• 2000

This paper presents the modeling method of nonlinear circuits with lumped elements by using a hybrid Haar -wavelet MRTD/FDTD techniques. To analyze nonlinear circuits with lumped elements, the Haar-wavelet MRTD scheme is applied to the entire structure of interest and the conventional FDTD scheme is locally used to describe the characteristics of the lumped elements. To validate the scheme, microstrip structure with lumped elements and a single diode mixer are simulated.

• Proceedings of the KIEE Conference
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• 1997.07b
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• pp.425-427
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• 1997

In this paper, a new approach to modeling and control of nonlinear systems using fuzzy theory is presented. To express the various and complex behavior of nonlinear system, we combine multiple model method with hierachical prioritized structure. The mountain clustering technique is used in partitioning of system, and TSK rule structure is adopted to form the fuzzy rules. Also we soften the paradigm of Mamdani's inference mechanism by using Yager's S-OWA operators.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 1996.10a
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• pp.302-305
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• 1996

In this paper, an optimal identification method using fuzzy-neural networks is proposed for modeling of nonlinear complex systems. The proposed fuzzy-neural modeling implements system structure and parameter identification using the intelligent schemes together with optimization theory, linguistic fuzzy implication rules, and neural networks(NNs) from input and output data of processes. Inference type for this fuzzy-neural modeling is presented as simplified inference. To obtain optimal model, the learning rates and momentum coefficients of fuzz-neural networks(FNNs) and parameters of membership function are tuned using genetic algorithm(GAs). For the purpose of its application to nonlinear processes, data for route choice of traffic problems and those for activated sludge process of sewage treatment system are used for the purpose of evaluating the performance of the proposed fuzzy-neural network modeling. The show that the proposed method can produce the intelligence model w th higher accuracy than other works achieved previously.

• Structural Engineering and Mechanics
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• v.43 no.2
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• pp.163-177
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• 2012

This paper presents a LMI(linear matrix inequality)-based fuzzy approach of modeling and active vibration control of geometrically nonlinear flexible plates with piezoelectric materials as actuators and sensors. The large-amplitude vibration characteristics and dynamic partial differential equation of a piezoelectric flexible rectangular thin plate structure are obtained by using generalized Fourier series and numerical integral. Takagi-Sugeno (T-S) fuzzy model is employed to approximate the nonlinear structural system, which combines the fuzzy inference rule with the local linear state space model. A robust fuzzy dynamic output feedback control law based on the T-S fuzzy model is designed by the parallel distributed compensation (PDC) technique, and stability analysis and disturbance rejection problems are guaranteed by LMI method. The simulation result shows that the fuzzy dynamic output feedback controller based on a two-rule T-S fuzzy model performs well, and the vibration of plate structure with geometrical nonlinearity is suppressed, which is less complex in computation and can be practically implemented.