• Proceedings of the KIEE Conference
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• 1998.07b
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• pp.581-584
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• 1998

This paper presents a scheme for fuzzy modelling and control of continuous-time nonlinear systems using a genetic algorithm. A fuzzy model is characterized by fuzzy "if-then" rules whose consequence part has a linear dynamic equation as subsystem of the system. The parameters of the fuzzy model are adjusted by a genetic algorithm. Then a tracking controller which guarantees stability of the overall system is designed. The simulation result demonstrates the effectiveness of the proposed method.

• Proceedings of the Computational Structural Engineering Institute Conference
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• 1993.04a
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• pp.141-148
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• 1993

In this paper, TVLEM is selected for the shear wall model which was proposed by Kabeyasawa and the characteristics of spring models composing TVLEM was studied. In axial stiffness spring model, the horizontal displacements when Kabeyasawa model and simple axial stiffness hysteresis model were used, were closely similar. When the large shear-displacement was occured, stiffness degrading model was more adquate to the shear wall modelling than OOHM. Also for the purpose of modelling the horizontally continuous wall, the seperational method for TVLEM was used. The results of nonlinear analysis by this method were closely similar to experimental results .

• Computers and Concrete
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• v.25 no.4
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• pp.369-382
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• 2020

This paper presents a discussion of the Finite Element Analysis (FEA) when applied for the analysis of concrete elements reinforced with glass fibre reinforced polymer (GFRP) bars. The purpose of such nonlinear FEA model development is to create a tool that can be used for numerical parametric studies which can be used to extend the existing (and limited) experiment database. The presented research focuses on the numerical analyses of concrete beams reinforced with GFRP longitudinal and shear reinforcements. FEA of concrete members reinforced with linear elastic brittle reinforcements (like GFRP) presents unique challenges when compared to the analysis of members reinforced with plastic (steel) reinforcements, which are discussed in the paper. Specifically, the behaviour and failure of GFRP reinforced members are strongly influenced by the compressive response of concrete and thus modelling of concrete behaviour is essential for proper analysis. FEA was performed using the commercial software ABAQUS. A damaged-plasticity model was utilized to simulate the concrete behaviour. The influence of tension, compression, dilatancy, mesh, and reinforcement modelling was studied to replicate experimental test data of beams previously tested at the University of Waterloo, Canada. Recommendations for the finite element modelling of beams reinforced with GFRP longitudinal and shear reinforcements are offered. The knowledge gained from this research allows for the development of a rational methodology for modelling GFRP reinforced concrete beams, which subsequently can be used for extensive parametric studies and the formation of informed recommendations to design standards.

• Journal of the Korean Institute of Intelligent Systems
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• v.27 no.1
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• pp.50-58
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• 2017

For nonlinear control system design, there are many studies based on TSK fuzzy model. However, TSK fuzzy modelling needs nonlinear dynamic equations of the object system or a data set fully distributed in input-output space. This paper proposes an modelling technique using only step input response data. The technique uses also the genetic algorithm. The object systems in this paper are nonlinear to control input variable or output variable. In the case of nonlinear to control input, response data obtained with several step input values are used. In the case of nonlinear to output, step input response data and zero input response data are used. This paper also presents a fuzzy controller design technique from TSK fuzzy model. The effectiveness of the proposed techniques is verified with numerical examples.

• Earthquakes and Structures
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• v.14 no.6
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• pp.551-565
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• 2018

Experimental testing is considered the most realistic approach to obtain a detailed representation of the nonlinear behaviour of masonry-infilled reinforced concrete (RC) structures. Among other applications, these tests can be used to calibrate the properties of numerical models such as simplified macro-models (e.g., strut-type models) representing the masonry infill behaviour. Since the significant cost of experimental tests limits their widespread use, alternative approaches need to be established to obtain adequate data to validate the referred simplified models. The proposed paper introduces a detailed finite element modelling strategy that can be used as an alternative to experimental tests to represent the behaviour of masonry-infilled RC frames under earthquake loading. Several examples of RC infilled frames with different infill configurations and properties subjected to cyclic loading are analysed using the proposed modelling approach. The comparison between numerical and experimental results shows that the numerical models capture the overall nonlinear behaviour of the physical specimens with adequate accuracy, predicting their monotonic stiffness, strength and several failure mechanisms.

• Smart Structures and Systems
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• v.33 no.2
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• pp.133-143
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• 2024

Piezoelectric (PZT) actuators have been widely used in precision positioning fields for their excellent displacement resolution. However, due to the inherent characteristics of piezoelectric actuators, hysteresis has been proven to greatly reduce positioning performance. In this paper, five mathematical hysteretic models based on activation function are proposed to characterize the nonlinear hysteresis characteristics of piezoelectric actuators. Then the performance of the proposed models is verified by particle swarm optimization (PSO) algorithm and the experiment data. Thirdly, the fitting performance of the proposed models is compared with the classical Bouc-Wen model. Finally, the performance of the five proposed models in modelling hysteresis nonlinearity of piezoelectric drivers is compared, in terms of RMSE, MAPE, SAPE and operation efficiency, and relevant suggestions are given.

• 제어로봇시스템학회:학술대회논문집
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• 2001.10a
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• pp.102.4-102
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• 2001

An evolutionary structure optimization method for the Gaussian radial basis function (RBF) network is presented, for modelling and predicting nonlinear time series. Generalization performance is significantly improved with a much smaller network, compared with that of the usual clustering and least square learning method.

• Proceedings of the Korean Society of Precision Engineering Conference
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• 1995.10a
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• pp.24-27
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• 1995

This paper presents, step-by-step, the capabilities that a general-purpose simulation environment, such as Simulab/Matlab,provides for an intuitive and sfficient modelling of grinding processes. Starting from a revision of the different approaches which can go found in the technical literature the paper begins with the well-known block-diagram forst presented by Snoyes and how the different parameters for the simulation are introduced in the model(machine,grinding wheel and process parameters). Special attention is paid to the fact that nonlinear phenomena can easily be include.

• 제어로봇시스템학회:학술대회논문집
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• 2003.10a
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• pp.1443-1448
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• 2003

Model Predictive Control (MPC) has recently found wide acceptance in industrial applications, but its potential has been much impounded by linear models due to the lack of a similarly accepted nonlinear modelling or data based technique. The authors have recently developed a new method for obtaining Volterra kernels of up to third order by use of pseudorandom M-sequence. By use of this method, nonparametric NMPC is derived in discrete-time using multi-dimensional convolution between plant data and Volterra kernel measurements. This approach is applied to an industrial polymerisation process using Volterra kernels of up to the third order. Results show that the nonparametric approach is very efficient and effective and considerably outperforms existing methods, while retaining the original data-based spirit and characteristics of linear MPC.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.14 no.7
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• pp.1544-1550
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• 2010

There have been many researches applying GMDH for modelling nonlinear dynamic systems. However, these methods require a great amount of computation in return of the accuracy. Thus, in this paper, we propose a method to reduce the amount of computation in GMDH by adjusting the adopting criterion of input data in decrement while at least maintaining the accuracy. The simulation result verifies that the proposed method can successfully reduce the amount of computation without the expense of the error rate, if not significantly better.