• Journal of Drive and Control
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• v.14 no.2
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• pp.16-22
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• 2017

This paper presents the algorithm of an adaptive model-free-control-based steering control for multi-axle all-terrain cranes for which the recursive least squares with forgetting are applied. To optimally control the actual system in the real world, the linear or nonlinear mathematical model of the system should be given for the determination of the optimal control inputs; however, it is difficult to derive the mathematical model due to the actual system's complexity and nonlinearity. To address this problem, the proposed adaptive model-free controller is used to control the steering angle of a multi-axle crane. The proposed model-free control algorithm uses only the input and output signals of the system to determine the optimal inputs. The recursive least-squares algorithm identifies first-order systems. The uncertainty between the identified system and the actual system was estimated based on the disturbance observer. The proposed control algorithm was used for the steering control of a multi-axle crane, where only the steering input and the desired yaw rate were employed, to track the reference path. The controller and performance evaluations were constructed and conducted in the Matlab/Simulink environment. The evaluation results show that the proposed adaptive model-free-control-based steering-control algorithm produces a sound path-tracking performance.

• Journal of Korea Society of Industrial Information Systems
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• v.21 no.4
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• pp.31-41
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• 2016

This paper is to evaluate the performance of a proposed hybrid genetic algorithm (pro-HGA) approach using closed-loop supply chain (CLSC) model. The proposed CLSC model is a integrated supply chain network model both with forward logistics and reverse logistics. In the proposed CLSC model, the reuse, resale and waste disposal using the returned products are taken into consideration. For implementing the proposed CLSC model, two conventional approaches and the pro-HGA are used in numerical experiment and their performances are compared with each other using various measures of performance. The experimental results show that the pro-HGA approach is more efficient in locating optimal solution than the other competing approaches.

• Journal of applied mathematics & informatics
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• v.26 no.3_4
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• pp.493-500
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• 2008

In this paper, we first construct a mathematical model for tagSNP selection based on LD measure $r^2$, then aiming at this kind of model, we develop an efficient algorithm, which is called approximate greedy algorithm. This algorithm is able to make up the disadvantage of the greedy algorithm for tagSNP selection. The key improvement of our approximate algorithm over greedy algorithm lies in that it adds local replacement(or local search) into the greedy search, tagSNP is replaced with the other SNP having greater similarity degree with it, and the local replacement is performed several times for a tagSNP so that it can improve the tagSNP set of the local precinct, thereby improve tagSNP set of whole precinct. The computational results prove that our approximate greedy algorithm can always find more efficient solutions than greedy algorithm, and improve the tagSNP set of whole precinct indeed.

• Proceedings of the SAREK Conference
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• 2009.06a
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• pp.161-166
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• 2009

Condensing gas boiler units may make a big role for the reduction of energy consumption in heating industries. In order to decrease the energy consumption of a boiler unit, the effective operation is necessary. In this study, the supply water temperature algorithm of a condensing gas boiler was developed. This includes the setpoint algorithm and the control algorithm of the supply water temperature. The setpoint algorithm was developed by the fuzzy logic and the control algorithm was developed by the proportional integral algorithm. In order to analyse the performance of the supply water temperature algorithm, the dynamic model of a condensing gas boiler system was used. Simulation results showed that the supply water temperature algorithm developed for this study may be practically applied for the control of the condensing gas boiler.

• Journal of Korea Society of Industrial Information Systems
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• v.22 no.2
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• pp.79-89
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• 2017

The Optimization of a Closed-Loop Supply Chain (CLSC) Model Using an Adaptive Hybrid Genetic Algorithm (AHGA) Approach is Considered in this Paper. With Forward and Reverse Logistics as an Integrated Logistics Concept, The CLSC Model is Consisted of Various Facilities Such as Part Supplier, Product Manufacturer, Collection Center, Recovery Center, etc. A Mathematical Model and the AHGA Approach are Used for Representing and Implementing the CLSC Model, Respectively. Several Conventional Approaches Including the AHGA Approach are Used for Comparing their Performances in Numerical Experiment.

• Geomechanics and Engineering
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• v.10 no.1
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• pp.37-48
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• 2016

The hyperbolic stress-strain model has been shown to be valid for modeling nonlinear stress-strain behavior for rockfill materials. The Duncan-Chang nonlinear constitutive model was adopted to characterize the behavior of the modeled rockfill materials in this study. Accurately estimating the model parameters of rockfill materials is a key problem for simulating dam deformations during both the dam construction period and the dam operation period. In order to estimate model parameters, triaxial compression experiments of rockfill materials were performed. Based on a genetic algorithm, the constitutive model parameters of the rockfill material were determined from the triaxial compression experimental data. The investigation results show that the predicted strains provide satisfactory precision when compared with the observed strains and the strains forecasted by a gradient-based optimization algorithm. The effectiveness of the proposed inversion procedure of model parameters was verified by experimental investigation in a laboratory.

• Proceedings of the Computational Structural Engineering Institute Conference
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• 2008.04a
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• pp.156-159
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• 2008

This paper introduces a remained tensile force estimation method using SI technique based on evolutionary algorithm for externally prestressed tendon. This paper applies the differential evolutionary scheme to SI technique. A virtual model test using ABAQUS 3 dimensional frame model has been made for this work The virtual model is added to the tensile force(28.5kN). Two set of frequencies are extracted respectively from the virtual test and the self-coding FEM 2 dimension model. The estimating tendon tension for the FEM model is 28.31kN. It is that the error in the tendon tension is 1% through the differential evolutionary algorithm. The errors between virtual model and the self-coding FEM model are assumed as the model error.

• Journal of Mechanical Science and Technology
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• v.20 no.12
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• pp.2034-2042
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• 2006

In this study, a complete 3D surface reconstruction method is proposed based on the concept that the vertices, of surface model can be completely matched to the unstructured point cloud. In order to generate the initial mesh model from the point cloud, the mesh subdivision of bounding box and shrink-wrapping algorithm are introduced. The control mesh model for well representing the topology of point cloud is derived from the initial mesh model by using the mesh simplification technique based on the original QEM algorithm, and the parametric surface model for approximately representing the geometry of point cloud is derived by applying the local subdivision surface fitting scheme on the control mesh model. And, to reconstruct the complete matching surface model, the insertion of isolated points on the parametric surface model and the mesh optimization are carried out. Especially, the fast 3D surface reconstruction is realized by introducing the voxel-based nearest-point search algorithm, and the simulation results reveal the availability of the proposed surface reconstruction method.

• Smart Structures and Systems
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• v.25 no.5
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• pp.559-567
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• 2020

Vibration at the tip of various flexible manipulators may affect their operation accuracy and work efficiency. To suppress such vibrations, the feasibility of using MFC actuators and sensors is investigated in this paper. Considering the convergence of the famous filtered-x least mean square (FXLMS) algorithm could not be guaranteed while it is employed for vibration suppression of plants with varying secondary path, this paper proposes a new multiple model switching adaptive control algorithm to implement the real time active vibration suppression tests with a new multiple switching strategy. The new switching strategy is based on a cost function with reconstructed error signal and disturbance signal instead of the error signal from the error sensor. And from a robustness perspective, a new variable step-size sign algorithm (VSSA) based FXLMS algorithm is proposed to improve the convergence rate. A cantilever beam with varying tip mass is employed as flexible manipulator model. MFC layers are attached on both sides of it as sensors and actuators. A co-simulation platform was built using ADAMS and MATLAB to test the feasibility of the proposed algorithms. And an experimental platform was constructed to verify the effectiveness of MFC actuators and sensors and the real-time vibration control performance. Simulation and experiment results show that the proposed FXLMS algorithm based multiple model adaptive control approach has good convergence performance under varying load conditions for the flexible cantilever beam, and the proposed FX-VSSA-LMS algorithm based multiple model adaptive control algorithm has the best vibration suppression performance.

• Journal of the Korean Institute of Intelligent Systems
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• v.9 no.5
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• pp.555-565
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• 1999

The optimal identification algorithm of fuzzy systems is presented for rule-based fuzzy modeling of nonlinear complex systems. Nonlinear systems are expressed using the identification of structure such as input variables and fuzzy input subspaces, and parameters of a fuzzy model. In this paper, the rule-based fuzzy modeling implements system structure and parameter identification using the fuzzy inference methods and hybrid structure combined with two types of optimization theories for nonlinear systems. Two types of inference methods of a fuzzy model are the simplified inference and linear inference. The proposed hybrid optimal identification algorithm is carried out using both a genetic algorithm and the improved complex method. Here, a genetic algorithm is utilized for determining initial parameters of membership function of premise fuzzy rules, and the improved complex method which is a powerful auto-tuning algorithm is carried out to obtain fine parameters of membership function. Accordingly, in order to optimize fuzzy model, we use the optimal algorithm with a hybrid type for the identification of premise parameters and standard least square method for the identification of consequence parameters of a fuzzy model. Also, an aggregate performance index with weighting factor is proposed to achieve a balance between performance results of fuzzy model produced for the training and testing data. Two numerical examples are used to evaluate the performance of the proposed model.