scholarly journals Position - Velocity Control for Two PMDC Motors Connected by a Cross-Coupling Technique with Butterflies Optimization Algorithm

2021 ◽  
Vol 3 (2) ◽  
pp. 31-44
Author(s):  
Kareem Ghazi Abdulhussein ◽  
Naseer M. Yasin ◽  
Ihsan J. Hasan
Keyword(s):  
Optimization Algorithm ◽  
Optimal Parameter ◽  
Cross Coupling ◽  
Absolute Error ◽  
Velocity Control ◽  
Coupling Technique ◽  
Dc Motors ◽  
Working Together ◽  
Permanent Magnet Dc Motors ◽  
Parameter Values

In this paper, two contributions are presented. the first is to design two cascade controllers to control the velocity and position for two Permanent Magnet DC motors (PMDC) working together at the same time for use in many applications such as CNC machines, robotics, and others. Furthermore, the cross-coupling technique is used to connect these motors and adjust the precise synchronization of their movement on the axes. The second contribution is the use of the butterfly’s optimization algorithm (BOA) with the objective function Integral Time Absolute Error (ITAE) to extract the optimal parameter values for the two cascade controllers and the synchronization controller in order to obtain the best accurate results. The simulation results showed high accuracy to reach the desired position at a regular velocity of both the PMDC motors with accurate synchronization and tracking trajectory on the axes. In addition, a very small position deviation of 0.021 rad was observed, and the system returned to a steady-state after 2 seconds of applying the full load.

scholarly journals Parameter Optimization for Ship Antiroll Gyros

2020 ◽  
Vol 10 (2) ◽  
pp. 661
Author(s):  
Yuanyuan Zhu ◽  
Shijie Su ◽  
Yuchen Qian ◽  
Yun Chen ◽  
Wenxian Tang
Keyword(s):  
Parameter Optimization ◽  
Optimization Algorithm ◽  
Optimal Parameter ◽  
Reduction Rate ◽  
Pattern Search ◽  
Random Waves ◽  
Search Optimization ◽  
Bacteria Foraging Optimization Algorithm ◽  
Parameter Values ◽  
Better Than

Ship antiroll gyros are a type of equipment used to reduce ships’ roll angle, and their parameters are related to the parameters of a ship and wave, which affect gyro performance. As an alternative framework, we designed a calculation method for roll reduction rate and considered random waves to establish a gyro parameter optimization model, and we then solved it through the bacteria foraging optimization algorithm (BFOA) and pattern search optimization algorithm (PSOA) to obtain optimal parameter values. Results revealed that the two methods could effectively reduce the overall mass and floor space of the antiroll gyro and improved its antirolling effect. In addition, the convergence speed and antirolling effect of the BFOA were better than that of the PSOA.

scholarly journals Differential Transform Method as an Effective Tool for Investigating Fractional Dynamical Systems

2021 ◽  
Vol 11 (15) ◽  
pp. 6955
Author(s):  
Andrzej Rysak ◽  
Magdalena Gregorczyk
Keyword(s):  
Optimal Parameter ◽  
Differential Transform Method ◽  
Bifurcation Diagrams ◽  
Transform Method ◽  
Rossler System ◽  
Differential Transform ◽  
Rössler System ◽  
Fractional System ◽  
Parameter Values

This study investigates the use of the differential transform method (DTM) for integrating the Rössler system of the fractional order. Preliminary studies of the integer-order Rössler system, with reference to other well-established integration methods, made it possible to assess the quality of the method and to determine optimal parameter values that should be used when integrating a system with different dynamic characteristics. Bifurcation diagrams obtained for the Rössler fractional system show that, compared to the RK4 scheme-based integration, the DTM results are more resistant to changes in the fractionality of the system.

scholarly journals Improved model predictive load frequency control of interconnected power system with synchronized automatic generation control loops

2020 ◽  
Vol 9 (1) ◽  
Author(s):  
Abdullahi Bala Kunya ◽  
Mehmet Argin ◽  
Yusuf Jibril ◽  
Yusuf Abubakar Shaaban
Keyword(s):  
Frequency Control ◽  
Cross Coupling ◽  
Absolute Error ◽  
Automatic Generation ◽  
Load Frequency Control ◽  
Automatic Generation Control ◽  
Control Vector ◽  
Interconnected Power System ◽  
Control Scheme ◽  
Generation Control

Abstract Background Automatic generation control (AGC) of multi-area interconnected power system (IPS) is often designed with negligible cross-coupling between the load frequency control (LFC) and automatic voltage regulation (AVR) loops. This is because the AVR loop is considerably faster than that of LFC. However, with the introduction of slow optimal control action on the AVR, positive damping effect can be achieved on the LFC loop thereby improving the frequency control. In this paper, LFC synchronized with AVR in three-area IPS is proposed. Model predictive controller (MPC) configured in a dense distributed pattern, due to its online set-point tacking is used as the supplementary controller. The dynamics of the IPS subjected to multi-area step and random load disturbances are studied. The efficacy of the developed scheme is ascertained by simulating the disturbed system in MATLAB/Simulink. Results Based on the comparative analysis on the system responses, it is established that by cross-coupling the LFC loop with AVR, reductions of 66.45% and 59.09% in the frequency and tie-line power maximum deviations respectively are observed, while the respective settling times are found to be reduced by 29.68% and 22.77% when compared with the uncoordinated control scheme. In addition, the standard deviation and variance of the integral time absolute error of the system’s responses have reduced by 23.21% and 20.83% respectively compared to those obtained in a similar study. Conclusions The reduction in the maximum deviations and settling times in the system states indicates that introducing the voltage control via AVR loop has improved the frequency control significantly. While the lower standard deviation and variance of the integral time absolute error signify improvement in the robustness of the developed algorithm. However, this improvement is at the detriment of the controller size and computational complexity. In the uncoordinated control scheme, the control vector is one-dimensional, while in the coordinated scheme, the control vector is two-dimensional for each CA.

scholarly journals Selecting single cell clustering parameter values using subsampling-based robustness metrics

2021 ◽  
Vol 22 (1) ◽  
Author(s):  
Ryan B. Patterson-Cross ◽  
Ariel J. Levine ◽  
Vilas Menon
Keyword(s):  
Single Cell ◽  
Optimal Parameter ◽  
Clustering Algorithms ◽  
Cell Types ◽  
Parameter Selection ◽  
Data Set ◽  
Biologically Relevant ◽  
Cell Clustering ◽  
Parameter Values ◽  
Robustness Metrics

Abstract Background Generating and analysing single-cell data has become a widespread approach to examine tissue heterogeneity, and numerous algorithms exist for clustering these datasets to identify putative cell types with shared transcriptomic signatures. However, many of these clustering workflows rely on user-tuned parameter values, tailored to each dataset, to identify a set of biologically relevant clusters. Whereas users often develop their own intuition as to the optimal range of parameters for clustering on each data set, the lack of systematic approaches to identify this range can be daunting to new users of any given workflow. In addition, an optimal parameter set does not guarantee that all clusters are equally well-resolved, given the heterogeneity in transcriptomic signatures in most biological systems. Results Here, we illustrate a subsampling-based approach (chooseR) that simultaneously guides parameter selection and characterizes cluster robustness. Through bootstrapped iterative clustering across a range of parameters, chooseR was used to select parameter values for two distinct clustering workflows (Seurat and scVI). In each case, chooseR identified parameters that produced biologically relevant clusters from both well-characterized (human PBMC) and complex (mouse spinal cord) datasets. Moreover, it provided a simple “robustness score” for each of these clusters, facilitating the assessment of cluster quality. Conclusion chooseR is a simple, conceptually understandable tool that can be used flexibly across clustering algorithms, workflows, and datasets to guide clustering parameter selection and characterize cluster robustness.

DEFORMABLE MESH FOR AUTOMATED SURFACE EXTRACTION FROM NOISY IMAGES

2004 ◽  
Vol 04 (03) ◽  
pp. 405-432 ◽  
Author(s):  
JUSSI TOHKA ◽  
JOUNI M. MYKKÄNEN
Keyword(s):  
Global Optimization ◽  
Internal Energy ◽  
Optimization Algorithm ◽  
Automatic Segmentation ◽  
Medical Image Analysis ◽  
Surface Extraction ◽  
Deformable Surface ◽  
Surface Models ◽  
Deformable Mesh ◽  
Parameter Values

Surface extraction from noisy volumetric images is a problem commonly encountered in medical image analysis. Deformable surface models can, in principle, solve the problem in an automatic manner. However, it is often essential that a reasonably close initialization and good parameter values for deformable models are provided. In this paper, novel algorithms for global minimization of the energy of deformable meshes are presented. We demonstrate that global optimization by these algorithms reduces the sensitivity of the deformable mesh to its initialization and its parameter values. Consequently, it becomes easier to automate the initialization process and the selection of parameter values. As the second contribution, the internal energy function is derived in a novel way in the framework of deformable surface models. The construction of the internal energy in this way features a simple way to derive the variants of our global optimization algorithm. The experiments with synthetic images are performed to compare variants of the proposed optimization algorithm. Also, we present a practical application of our deformable model to automatic segmentation of positron emission tomography images.

A numerical method for determining optimal parameter values in forced periodic operation

1992 ◽  
Vol 47 (3) ◽  
pp. 605-613 ◽  
Author(s):  
Fatih Özgülşen ◽  
Raymond A. Adomaitis ◽  
Ali Çinar
Keyword(s):  
Numerical Method ◽  
Optimal Parameter ◽  
Periodic Operation ◽  
Parameter Values
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