PSO algorithm-based robust design of PID controller for variable time-delay systems: AQM application

This paper formulates a robust control for variable time-delay system models. An automatic tuning method for PID-type controller is proposed. The adopted method integrates robust control design using Quantitative Feedback Theory (QFT) with Particle Swan Optimization heuristic algorithms (PSO) to systematize the loop-shaping stage. The objective of the design method is to reach a good compromise among robust stability, robust tracking and disturbance rejection with minimal control effort. The resulting algorithm has attractive features, such as easy implementation, stable convergence characteristic and good computational efficiency. In particular, the results of the control design for active queue management (AQM) systems are presented. Simulations show improved congestion control and quality of service in TCP communication networks.Facultad de Informátic

A Comprehensive Review and Analysis of Nanosensors for Structural Health Monitoring in Bridge Maintenance: Innovations, Challenges, and Future Perspectives

This paper presents a thorough review and detailed analysis of nanosensors for structural health monitoring (SHM) in the context of bridge maintenance. With rapid advancements in nanotechnology, nanosensors have emerged as promising tools for detecting and assessing the structural integrity of bridges. The objective of this review is to provide a comprehensive understanding of the various types of nanosensors utilized in bridge maintenance, their operating principles, fabrication techniques, and integration strategies. Furthermore, this paper explores the challenges associated with nanosensor deployment, such as signal processing, power supply, and data interpretation. Finally, the review concludes with an outlook on future developments in the field of nanosensors for SHM in bridge maintenance.publishedVersio

Benelux meeting on systems and control, 23rd, March 17-19, 2004, Helvoirt, The Netherlands

Book of abstract

Ant Colony Optimization

Ant Colony Optimization (ACO) is the best example of how studies aimed at understanding and modeling the behavior of ants and other social insects can provide inspiration for the development of computational algorithms for the solution of difficult mathematical problems. Introduced by Marco Dorigo in his PhD thesis (1992) and initially applied to the travelling salesman problem, the ACO field has experienced a tremendous growth, standing today as an important nature-inspired stochastic metaheuristic for hard optimization problems. This book presents state-of-the-art ACO methods and is divided into two parts: (I) Techniques, which includes parallel implementations, and (II) Applications, where recent contributions of ACO to diverse fields, such as traffic congestion and control, structural optimization, manufacturing, and genomics are presented