• Journal of the Korean Society for Aeronautical & Space Sciences
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• v.48 no.9
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• pp.691-701
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• 2020

An actuator mixer design using convex optimization technique situation where the propulsion system of an electric VTOL UAV during vertical take-off and landing maneuvers is proposed. The attainable control set to analyze the impact from failure of each motor and propeller can be calculated and illustrated using the properties of the convex function. The control allocation can be defined as a convex function optimization problem to obtain an optimal solution in real time. The mixer is implemented using a convex optimization solver, and the performance of the control allocation methods is compared to the attainable control set. Finally, the proposed mixer is compared with other techniques with nonlinear sux degree-of-freedom simulation.

• Earthquakes and Structures
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• v.11 no.6
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• pp.1001-1025
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• 2016

Building structures generally have inherent low damping capability and hence are vulnerable to seismic excitations. Control devices therefore play a useful role in providing safety to building structures subject to seismic events. In recent years semi-active dampers have gained considerable attention as structural control devices in the building construction industry. Magneto-rheological (MR) damper, a type of semi-active damper has proven to be effective in seismic mitigation of building structures. MR dampers contain a controllable MR fluid whose rheological properties vary rapidly with the applied magnetic field. Although some research has been carried out on the use of MR dampers in building structures, optimal design of MR damper and combined use of MR and passive dampers for real scale buildings has hardly been investigated. This paper investigates the use of MR dampers and incorporating MR-passive damper combinations in building structures in order to achieve acceptable levels of seismic performance. In order to do so, it first develops the MR damper model by integrating control algorithms commonly used in MR damper modelling. The developed MR damper is then integrated in to the seismically excited structure as a time domain function. Linear and nonlinear structure models are evaluated in real time scenarios. Analyses are conducted to investigate the influence of location and number of devices on the seismic performance of the building structure. The findings of this paper provide information towards the design and construction of earthquake safe buildings with optimally employed MR dampers and MR-passive damper combinations.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.59 no.2
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• pp.436-444
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• 2010

In this study, we introduce and discuss a concept of a granular-oriented radial basis function neural networks (GRBF NNs). In contrast to the typical architectures encountered in radial basis function neural networks(RBF NNs), our main objective is to develop a design strategy of GRBF NNs as follows : (a) The architecture of the network is fully reflective of the structure encountered in the training data which are granulated with the aid of clustering techniques. More specifically, the output space is granulated with use of K-Means clustering while the information granules in the multidimensional input space are formed by using a so-called context-based Fuzzy C-Means which takes into account the structure being already formed in the output space, (b) The innovative development facet of the network involves a dynamic reduction of dimensionality of the input space in which the information granules are formed in the subspace of the overall input space which is formed by selecting a suitable subset of input variables so that the this subspace retains the structure of the entire space. As this search is of combinatorial character, we use the technique of genetic optimization to determine the optimal input subspaces. A series of numeric studies exploiting some nonlinear process data and a dataset coming from the machine learning repository provide a detailed insight into the nature of the algorithm and its parameters as well as offer some comparative analysis.

• Smart Structures and Systems
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• v.23 no.1
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• pp.1-14
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• 2019

Control algorithms are the most important aspects in successful control of structures against earthquakes. In recent years, intelligent control methods rather than classical control methods have been more considered by researchers, due to some specific capabilities such as handling nonlinear and complex systems, adaptability, and robustness to errors and uncertainties. However, due to lack of learning ability of fuzzy controller, it is used in combination with a genetic algorithm, which in turn suffers from some problems like premature convergence around an incorrect target. Therefore in this research, the introduction and design of the Fuzzy Cooperative Coevolution (Fuzzy CoCo) controller and Adaptive Neural-Fuzzy Inference System (ANFIS) have been innovatively presented for semi-active seismic control. In this research, in order to improve the seismic behavior of structures, a semi-active control of building using Magneto Rheological (MR) damper is proposed to determine input voltage of Magneto Rheological (MR) dampers using ANFIS and Fuzzy CoCo. Genetic Algorithm (GA) is used to optimize the performance of controllers. In this paper, the design of controllers is based on the reduction of the Park-Ang damage index. In order to assess the effectiveness of the designed control system, its function is numerically studied on a 9-story benchmark building, and is compared to those of a Wavelet Neural Network (WNN), fuzzy logic controller optimized by genetic algorithm (GAFLC), Linear Quadratic Gaussian (LQG) and Clipped Optimal Control (COC) systems in terms of seismic performance. The results showed desirable performance of the ANFIS and Fuzzy CoCo controllers in considerably reducing the structure responses under different earthquakes; for instance ANFIS and Fuzzy CoCo controllers showed respectively 38 and 46% reductions in peak inter-story drift ($J_1$) compared to the LQG controller; 30 and 39% reductions in $J_1$ compared to the COC controller and 3 and 16% reductions in $J_1$ compared to the GAFLC controller. When compared to other controllers, one can conclude that Fuzzy CoCo controller performs better.

• Structural Engineering and Mechanics
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• v.90 no.1
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• pp.19-26
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• 2024

In recent years, an increasing number of experimental studies have shown that the practical application of mature active control systems requires consideration of robustness criteria in the design process, including the reduction of tracking errors, operational resistance to external disturbances, and measurement noise, as well as robustness and stability. Good uncertainty prediction is thus proposed to solve problems caused by poor parameter selection and to remove the effects of dynamic coupling between degrees of freedom (DOF) in nonlinear systems. To overcome the stability problem, this study develops an advanced adaptive predictive fuzzy controller, which not only solves the programming problem of determining system stability but also uses the law of linear matrix inequality (LMI) to modify the fuzzy problem. The following parameters are used to manipulate the fuzzy controller of the robotic system to improve its control performance. The simulations for system uncertainty in the controller design emphasized the use of acceleration feedback for practical reasons. The simulation results also show that the proposed H∞ controller has excellent performance and reliability, and the effectiveness of the LMI-based method is also recognized. Therefore, this dynamic control method is suitable for seismic protection of civil buildings. The objectives of this document are access to adequate, safe, and affordable housing and basic services, promotion of inclusive and sustainable urbanization, implementation of sustainable disaster-resilient construction, sustainable planning, and sustainable management of human settlements. Simulation results of linear and non-linear structures demonstrate the ability of this method to identify structures and their changes due to damage. Therefore, with the continuous development of artificial intelligence and fuzzy theory, it seems that this goal will be achieved in the near future.

• Journal of the Computational Structural Engineering Institute of Korea
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• v.29 no.6
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• pp.555-562
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• 2016

Seismic design of braced frames that simultaneously considers economic issues and structural performance represents a rather complicated engineering problem, and therefore, a systematic and well-established methodology is needed. This study proposes a multi-objective seismic design method for an inverted V-braced frame with suspended zipper struts that uses the non-dominated sorting genetic algorithm-II(NSGA-II). The structural weight and the maximum inter-story drift ratio as the objective functions are simultaneously minimized to optimize the cost and seismic performance of the structure. To investigate which of strength- and performance-based design criteria for braced frames is the critical design condition, the constraint conditions on the two design methods are simultaneously considered (i.e. the constraint conditions based on the strength and plastic deformation of members). The linear static analysis method and the nonlinear static analysis method are adopted to check the strength- and plastic deformation-based design constraints, respectively. The proposed optimal method are applied to three- and six-story steel frame examples, and the solutions improved for the considered objective functions were found.

• Journal of the Computational Structural Engineering Institute of Korea
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• v.28 no.2
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• pp.145-152
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• 2015

In the study, the effects of damping devices on damping ratio increase and wind-load reduction were investigated based on the computational platform, which is one of the parametric modeling methods. The computational platform helps the designers or engineers to evaluate the efficacy of the numerous alternative structural systems for irregular Super-Tall building, which is crucial in determining the capacity and the number of the supplemental damping devices for adding the required damping ratios to the building. The inherent damping ratio was estimated based on the related domestic and foreign researches conducted by using real wind-load records. Two types of damping devices were considered: One is inter-story installation type passive control devices and the other is mass type active control devices. The supplemental damping ratio due to the damping devices was calculated by means of equivalent static analysis using an equation suggested by FEMA. The optimal design of the damping devices was conducted by using the computational platform. The structural element quantity reduction effect resulting from the installation of the damping devices could be simply assessed by proposing a wind-load reduction factor, and the effectiveness of the proposed method was verified by a numerical example of a 455m high-rise building. The comparison between roof displacement and the story shear forces by the nonlinear time history analysis and the proposed method indicated that the proposed method could simply but approximately estimate the effects of the supplemental damping devices on the roof displacement and the member force reduction.

• Journal of Electrical Engineering and Technology
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• v.10 no.5
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• pp.1969-1982
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• 2015

The virtual inertia control (VIC) of wind turbine with directly-driven permanent-magnet synchronous generator (D-PMSG) can act similarly to the conventional synchronous generator in inertia response and frequency control, thereby supporting the system frequency stability. However, because the wind speed is inconstant and changeable to a certain extent and the D-PMSG is a complex nonlinear system, there are great difficulties in the virtual inertia optimal control of the D-PMSG. Based on the design principle of the active disturbance rejection control (ADRC), this paper presents a new VIC strategy for the D-PMSG from the perspective of power disturbance suppression in the system. The strategy helps fulfill the power grid disturbance estimation and compensation by means of the extended state observer (ESO) so as to improve the disturbance-resisting performance of the system. Compared with conventional proportional-derivative virtual inertia control (PDVIC), this method, which is of better adaptability and robustness, can not only improve the property of the D-PMSG responding to the system frequency but also reduce the influence of wind speed disturbance. The simulation and experiment results have verified the effectiveness and feasibility of the VIC based on the ADRC.

• Journal of the Korean Institute of Intelligent Systems
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• v.15 no.1
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• pp.81-86
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• 2005

In this study, we introduce a new category of fuzzy inference systems based on information granulation to carry out the model identification of complex and nonlinear systems. Informally speaking, information granules are viewed as linked collections of objects (data, in particular) drawn together by the criteria of proximity, similarity, or functionality Granulation of information with the aid of Hard C-Means (HCM) clustering help determine the initial parameters of fuzzy model such as the initial apexes of the membership functions and the initial values of polynomial functions being used in the premise and consequence part of the fuzzy rules. And the initial parameters are tuned effectively with the aid of the genetic algorithms(GAs) and the least square method (LSM). An aggregate objective function with a weighting factor is also used in order to achieve a balance between performance of the fuzzy model. The proposed model is evaluated with using a numerical example and is contrasted with the performance of conventional fuzzy models in the literature.

• Journal of the Korean Institute of Intelligent Systems
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• v.14 no.4
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• pp.402-410
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• 2004

In this paper, an optimal design method of clonal selection based Fuzzy-Neural Networks (FNN) model for complex and nonlinear systems is presented. The FNNs use the simplified inference as fuzzy inference method and Error Back Propagation Algorithm as learning rule. Also Advanced Clonal Selection (ACS) is proposed to find the parameters such as parameters of membership functions, learning rates and momentum coefficients. The proposed method is based on an Immune Algorithm (IA) using biological Immune System and The performance is improved by control of differentiation rate. Through that procedure, the antibodies are producted variously and the parameter of FNN are optimized by selecting method of antibody with the best affinity against antigens such as object function and limitation condition. To evaluate the performance of the proposed method, we use the time series data for gas furnace and traffic route choice process.