In this contribution, a methodology for the optimal tuning of controllers of complex systems based on meta-heuristic techniques is proposed. Two bio-inspired meta-heuristic optimization algorithms -the Antlion Optimizer (ALO) and the Whale Optimization Algorithm (WOA)- have been applied to two different dynamic systems: the Hoop & Ball electromechanical system, a system where a linearized description is adequate; and to a Wind Turbine-Generator-Rectifier, as an example of a complex non-linear dynamic system. The performance of the ALO and WOA techniques for the tuning of conventional PID controllers is evaluated in relation to the number of agents nS and the maximum number of iterations nMaxIter; given the stochastic nature of both methods, repeatability is also addressed. Finally, the computational effort required for their implementation is considered. By analyzing the obtained metrics, it is observed that both methods provide comparable results for the two systems considered and, therefore, the ALO and WOA techniques can complement each other by exploiting the advantages of each of them in controller tuning.
Download full-text PDF |
Source |
|---|---|
| http://dx.doi.org/10.3390/biomimetics10010013 | DOI Listing |
Publication Analysis
Top Keywords
Similar Publications
Enhancing Activation Energy Predictions under Data Constraints Using Graph Neural Networks.
J Chem Inf Model
January 2025
Department of Chemical Engineering, National Taiwan University, No. 1, Section 4, Roosevelt Road, Taipei 10617, Taiwan.
Accurately predicting activation energies is crucial for understanding chemical reactions and modeling complex reaction systems. However, the high computational cost of quantum chemistry methods often limits the feasibility of large-scale studies, leading to a scarcity of high-quality activation energy data. In this work, we explore and compare three innovative approaches (transfer learning, delta learning, and feature engineering) to enhance the accuracy of activation energy predictions using graph neural networks, specifically focusing on methods that incorporate low-cost, low-level computational data.
View Article and Find Full Text PDFMetabolic adaptations to acute glucose uptake inhibition converge upon mitochondrial respiration for leukemia cell survival.
Cell Commun Signal
January 2025
Department of Oncological Sciences, Icahn School of Medicine at Mount Sinai, One Gustave L. Levy Place, New York, NY, 10029, USA.
One hallmark of cancer is the upregulation and dependency on glucose metabolism to fuel macromolecule biosynthesis and rapid proliferation. Despite significant pre-clinical effort to exploit this pathway, additional mechanistic insights are necessary to prioritize the diversity of metabolic adaptations upon acute loss of glucose metabolism. Here, we investigated a potent small molecule inhibitor to Class I glucose transporters, KL-11743, using glycolytic leukemia cell lines and patient-based model systems.
View Article and Find Full Text PDFNovel continuous and quantitative intraoperative facial nerve-monitoring system for temporal bone lesions.
Eur Arch Otorhinolaryngol
January 2025
Department of Otorhinolaryngology-Head and Neck Surgery, Keio University School of Medicine, 35 Shinanomachi, Shinjuku-ku, Tokyo, 160-8582, Japan.
Objective: Intraoperative systems for monitoring facial nerve function, in which temporal electrical stimulation is applied to the facial nerve through electrodes, are used in many surgeries requiring facial nerve preservation; however, continuous stimulation or quantitative evaluation of facial nerve function is difficult with this approach. We examined the usefulness of a continuous and quantitative facial nerve-monitoring system for temporal bone lesions by using our experience to modify the existing methods used for cases involving vestibular schwannomas.
Study Design: Retrospective observational study.
A vision model for automated frozen tuna processing.
Sci Rep
January 2025
School of Food and Pharmacy, Zhejiang Ocean University, Zhoushan, 316022, People's Republic of China.
Accurate and rapid segmentation of key parts of frozen tuna, along with precise pose estimation, is crucial for automated processing. However, challenges such as size differences and indistinct features of tuna parts, as well as the complexity of determining fish poses in multi-fish scenarios, hinder this process. To address these issues, this paper introduces TunaVision, a vision model based on YOLOv8 designed for automated tuna processing.
View Article and Find Full Text PDFA quantum-optimized approach for breast cancer detection using SqueezeNet-SVM.
Sci Rep
January 2025
College of Information Science and Technology, Hainan Normal University, Haikou, 571158, China.
Breast cancer is one of the most aggressive types of cancer, and its early diagnosis is crucial for reducing mortality rates and ensuring timely treatment. Computer-aided diagnosis systems provide automated mammography image processing, interpretation, and grading. However, since the currently existing methods suffer from such issues as overfitting, lack of adaptability, and dependence on massive annotated datasets, the present work introduces a hybrid approach to enhance breast cancer classification accuracy.
View Article and Find Full Text PDFWant AI Summaries of new PubMed Abstracts delivered to your In-box?
Enter search terms and have AI summaries delivered each week - change queries or unsubscribe any time!