Parameters Tuning via Simplex-Search based Model-Free Optimization for the Steam Generator Level Control

Guan Jiansheng,Kong Xiansong 보안공학연구지원센터 2015 International Journal of Signal Processing, Image Vol.8 No.4

Control performance is critical to a control system. To improve the performance of the steam generator level control system, the control system parameters need to be optimized. Traditional parameters tuning methods, such as trial and error and Design of Experiments etc., are usually experience-based, cumbersome and time-consuming. To address the above inefficiencies, in this paper, the simplex-search based Model-Free Optimization (MFO) has been proposed to search for the optimal control system parameters. The optimized parameters will be gained to maximize the system’s control performance. Rather than traditional controller parameter tuning method, this method optimizes the control system by directly using measurements of control performance. An example of the PID parameters tuning for the steam generator level control was illustrated. The efficiency and the effectiveness of the Simplex-search based Model-Free Optimization – based control parameters tuning methodology has been verified through simulation experiments.

Optimization of parameters in mathematical models of biological systems

추상목,김영희 한국전산응용수학회 2008 Journal of applied mathematics & informatics Vol.26 No.1

Under pathological stress stimuli, dynamics of a biological system can be changed by alteration of several components such as functional proteins, ultimately leading to disease state. These dynamics in disease state can be modeled using differential equations in which kinetic or system parameters can be obtained from experimental data. One of the most effective ways to restore a particular disease state of biology system (i.e., cell, organ and organism) into the normal state makes optimization of the altered components usually represented by system parameters in the differential equations. There has been no such approach as far as we know. Here we show this approach with a cardiac hypertrophy model in which we obtain the existence of the optimal parameters and construct an optimal system which can be used to find the optimal parameters. Under pathological stress stimuli, dynamics of a biological system can be changed by alteration of several components such as functional proteins, ultimately leading to disease state. These dynamics in disease state can be modeled using differential equations in which kinetic or system parameters can be obtained from experimental data. One of the most effective ways to restore a particular disease state of biology system (i.e., cell, organ and organism) into the normal state makes optimization of the altered components usually represented by system parameters in the differential equations. There has been no such approach as far as we know. Here we show this approach with a cardiac hypertrophy model in which we obtain the existence of the optimal parameters and construct an optimal system which can be used to find the optimal parameters.

Optimal Design of a Planar-Type Antenna with a Reduced Number of Design Parameters Using Taguchi Method and Adaptive Particle Swarm Optimization

Jeong-Hyeok Lee,Dong-Hyeok Jang,Hyeong-Seok Kim 대한전기학회 2014 Journal of Electrical Engineering & Technology Vol.9 No.6

This paper presents a method to optimize the design of a planar-type antenna and reduce the number of design parameters for rapid computation. The electromagnetic characteristics of the structure are analyzed, and Taguchi method is used to identify critical design parameters. Adaptive particle swarm optimization, which has a faster convergence rate than particle swarm optimization, is used to achieve the design goal effectively. A compact dual-band USB dongle antenna is tested to verify the advantage of the proposed method. In this case, we can use only five selected geometrical parameters instead of eighteen to accelerate the optimization of the antenna design. The 10 dB bandwidth for return loss ranges from 2.3 GHz to 2.7 GHz and from 5.1 GHz to 5.9 GHz, covering all the WiBro, Bluetooth, WiMAX, and 802.11 b/g/n WLAN bands in both simulation and measurement. The optimization process enables the antenna design to achieve the required performance with fewer design parameters.

Optimal Design of a Planar-Type Antenna with a Reduced Number of Design Parameters Using Taguchi Method and Adaptive Particle Swarm Optimization

Lee, Jeong-Hyeok,Jang, Dong-Hyeok,Kim, Hyeong-Seok The Korean Institute of Electrical Engineers 2014 Journal of Electrical Engineering & Technology Vol.9 No.6

This paper presents a method to optimize the design of a planar-type antenna and reduce the number of design parameters for rapid computation. The electromagnetic characteristics of the structure are analyzed, and Taguchi method is used to identify critical design parameters. Adaptive particle swarm optimization, which has a faster convergence rate than particle swarm optimization, is used to achieve the design goal effectively. A compact dual-band USB dongle antenna is tested to verify the advantage of the proposed method. In this case, we can use only five selected geometrical parameters instead of eighteen to accelerate the optimization of the antenna design. The 10 dB bandwidth for return loss ranges from 2.3 GHz to 2.7 GHz and from 5.1 GHz to 5.9 GHz, covering all the WiBro, Bluetooth, WiMAX, and 802.11 b/g/n WLAN bands in both simulation and measurement. The optimization process enables the antenna design to achieve the required performance with fewer design parameters.

Development of Parameter Optimization System using Iterative Experiment and Optimization for Injection Foam Molding

Kyung-min Lee(이경민) 제어로봇시스템학회 2017 제어·로봇·시스템학회 논문지 Vol.23 No.12

Injection molding (IM) is one of the most important processes for mass-producing plastic products. There are several significant challenges in using IM. The IM process requires many input parameters, but the relationships between the desired material properties and parameter settings (e.g. gas content and pressure drop) are not well understood collectively. We propose an optimization-based computational framework that will provide computer-based decision support for setting parameters in the IM process. The decision support will enable dramatic time and cost efficiencies in that the settings for parameters. It can discover optimized parameters much more rapidly than conventional methods that require extensive experimentation. Key elements in the framework involve approximating the governing equations using analysis of variance (ANOVA) techniques and normative optimization modeling to achieve optimal parameter settings. We illustrate the computational framework on HDPE materials in which parameter settings such as gate geometry, N2 content, void fraction, and injection speed are considered. The proposed framework will provide an improved understanding of the relationships between material properties and parameter settings in general IM process environments.

Parameter Optimization of SVM Based on Improved ACO for Data Classification

Wen Chen,Yixiang Tian 보안공학연구지원센터 2016 International Journal of Multimedia and Ubiquitous Vol.11 No.1

The parameters of support vector machine have a great influence on the learning ability and generalization ability, so an improved ant colony optimization algorithm is proposed to optimize the parameters of SVM, then an optimized SVM classifier (IMACO-SVM) is proposed for data classification. In the IMACO-SVM, the adaptive adjustment pheromone strategy is used to make relatively uniform pheromone distribution and the improved pheromone updating method is used to submerge the heuristic factor by the residual pheromone information, in order to effectively solve the contradiction between expanding search and finding optimal solution. The selection of parameters of the SVM is regarded as a combination optimization of parameters in order to establish the objective function of combination optimization. The improved ACO algorithm with good robustness and positive feedback characteristics and parallel searching is used to search for the optimal value of objective function. In order to validate the classification effectiveness of the IMACO-SVM algorithm, some experimental data from the UCI machine learning database are selected in this paper. The classification results show that the proposed IMACO-SVM algorithm has higher classification ability and classification accuracy.

Parameters optimization design for LCL-type STATCOMs under complex power grid

Wang, Xiangyu,Wang, Minglei,Wang, Liguo,Fu, Guangjie,Qiao, Jinxin The Korean Institute of Power Electronics 2020 JOURNAL OF POWER ELECTRONICS Vol.20 No.2

LCL filter parameters and control parameters are interdependent and inter-restricted. They can all affect the stability of a static synchronous compensator (STATCOM) under the effects of a complex power grid such as harmonic grid voltage and grid impedance variation. An advanced parameters optimization method integrated with LCL filter and control strategy is proposed. At first, the ABC-Pareto algorithm (Pareto multi-objective optimization of an artificial bee colony algorithm) is used to reasonably choose the LCL filter parameters. Under the premise of using capacitor current feedback active damping control and grid voltage feedforward control, the mathematical models of the STATCOM are derived. The constraints of control performances on the control parameters are obtained. According to these constraints, it is possible to construct a satisfactory 3D space. The control parameters can be chosen reasonably by finding the optimized space when the power grid is changed. Simulation and experimental results show the effectiveness and superiority of the proposed method.

Data-Driven Cutting Parameters Optimization Method in Multiple Configurations Machining Process for Energy Consumption and Production Time Saving

Xikun Zhao,Congbo Li,Xingzheng Chen,Jiabin Cui,Bao Cao 한국정밀공학회 2022 International Journal of Precision Engineering and Vol.9 No.3

Cutting parameters and machining configurations affect the energy consumption and production time in the machining process significantly. Previous cutting parameters optimization methods are proposed for a specific machining configuration that limits its generalization ability. However, the machining configuration varies constantly with actual machining tasks, which results in the predetermined optimization method is impractical. We propose a data-driven optimization method for the multiple machining configurations, aimed at reducing energy consumption and production time. Firstly, the analysis of the relationship between energy consumption and meta-actions under different machining states is carried out, and the Gaussian process regression (GPR)-based energy consumption model is proposed. Then, a multi-objective optimization model is proposed for energy consumption and production time reduction, which is solved via a multi-objective grey wolf optimization. Finally, the experiments are conducted to verify the validity of the proposed method and the influence of metaactions on energy consumption and production time are explicitly analyzed. The case study indicates the proposed energy consumption model has better prediction accuracy for multiple machining configurations. Optimizing cutting parameters achieves a trade-off between energy consumption and production time. Moreover, the parametric influence indicates cutting speed is the most influential cutting parameter for energy consumption and production time.

Collaborative optimization for ring-stiffened composite pressure hull of underwater vehicle based on lamination parameters

Li, Bin,Pang, Yong-jie,Cheng, Yan-xue,Zhu, Xiao-meng The Society of Naval Architects of Korea 2017 International Journal of Naval Architecture and Oc Vol.9 No.4

A Collaborative Optimization (CO) methodology for ring-stiffened composite material pressure hull of underwater vehicle is proposed. Structural stability and material strength are both examined. Lamination parameters of laminated plates are introduced to improve the optimization efficiency. Approximation models are established based on the Ellipsoidal Basis Function (EBF) neural network to replace the finite element analysis in layout optimizers. On the basis of a two-level optimization, the simultaneous structure material collaborative optimization for the pressure vessel is implemented. The optimal configuration of metal liner and frames and composite material is obtained with the comprehensive consideration of structure and material performances. The weight of the composite pressure hull decreases by 30.3% after optimization and the validation is carried out. Collaborative optimization based on the lamination parameters can optimize the composite pressure hull effectively, as well as provide a solution for low efficiency and non-convergence of direct optimization with design variables.

Collaborative optimization for ring-stiffened composite pressure hull of underwater vehicle based on lamination parameters

BIN LI,Yong-jie Pang,Yan-xue Cheng,Xiao-meng Zhu 대한조선학회 2017 International Journal of Naval Architecture and Oc Vol.9 No.4

A Collaborative Optimization (CO) methodology for ring-stiffened composite material pressure hull of underwater vehicle is proposed. Structural stability and material strength are both examined. Lamination parameters of laminated plates are introduced to improve the optimization efficiency. Approximation models are established based on the Ellipsoidal Basis Function (EBF) neural network to replace the finite element analysis in layout optimizers. On the basis of a two-level optimization, the simultaneous structure material collaborative optimization for the pressure vessel is implemented. The optimal configuration of metal liner and frames and composite material is obtained with the comprehensive consideration of structure and material performances. The weight of the composite pressure hull decreases by 30.3% after optimization and the validation is carried out. Collaborative optimization based on the lamination parameters can optimize the composite pressure hull effectively, as well as provide a solution for low efficiency and non-convergence of direct optimization with design variables.