Topic Editors

Centro de Innovación y Desarrollo Tecnológico en Cómputo, Instituto Politécnico Nacional, Mexico City 07700, Mexico
Colegio de Ciencia y Tecnología, Universidad Autónoma de la Ciudad de Mexico, Mexico City 06720, Mexico
Artificial Intelligence Research Institute, University of Veracruz, Xalapa, Veracruz 91097, Mexico

Metaheuristics for the Planning, Design and Control of Mechatronic Systems with Energy Efficiency in Engineering Applications

Abstract submission deadline
closed (31 July 2023)
Manuscript submission deadline
closed (31 October 2023)
Viewed by
6045

Topic Information

Dear Colleagues, 

Current applications in engineering that are based on the use of mechatronic systems demand the fulfillment of various performance requirements. In order to meet these requirements, it is necessary to optimize one or several stages in the development of such systems, which may include task planning, design, and control. Regardless of the stage, the optimization is oriented towards increased precision, cost reduction, resource utilization, etc., which translates, directly or indirectly, into efficient energy consumption and management. Due to the nature of mechatronic systems, the optimization problems involved in their development are complex. For instance, those might involve topics from different mechatronic disciplines such as the energy conversion and management from the electrical engineering perspective; in the mechanical engineering branch with the reduction of the torque and forces produced by the mechanical structure; in the control area by tuning the controller gains of systems; in the computer science by making the shortest path planning, among others.

Despite their complexity, these problems can be handled by sophisticated computational techniques such as metaheuristics. Meta-heuristic algorithms are valuable tools that can be implemented to find innovative solutions in complicated search spaces based on phenomena and behaviors observed in nature and society. In addition, they can provide novelty and improved design solutions for different applications. 

This Topic invites contributions addressing recent research results on energy optimization both direct and indirect, in the planning, design, and control optimization of mechatronic systems using novel metaheuristic strategies. 

Topics include but are not limited to: 

  • Optimized task and path planning for robots.
  • Optimized controller tuning. 
  • Optimized mechanical, electronic, control, or integrated design 
  • Applications of optimized mechatronic systems in engineering, medicine, rehabilitation, etc.
  • Applications for computational intelligence in system optimization. 
  • Engineering optimization through metaheuristics. 
  • Novel metaheuristics for mechatronic system optimization. 
  • Multidisciplinary optimization for mechatronic systems
  • Optimized energy power systems for mechatronic systems
  • Optimized planning and management of energy for mechatronic systems

Once papers are accepted according to the publication times outlined at https://www.mdpi.com/journal/energies, they will be published on a rolling basis. We look forward to receiving your submissions.

Prof. Dr. Miguel Gabriel Villarreal-Cervantes
Dr. Alejandro Rodríguez-Molina
Dr. Efrén Mezura-Montes
Topic Editors

Keywords

  • energy optimization
  • metaheuristic algorithms
  • optimization
  • engineering applications
  • mechatronic systems
  • planning, design, and control
  • integrated design

Participating Journals

Journal Name Impact Factor CiteScore Launched Year First Decision (median) APC
Automation
automation
- 2.9 2020 26.3 Days CHF 1000
Energies
energies
3.0 6.2 2008 16.1 Days CHF 2600
Mathematics
mathematics
2.3 4.0 2013 16.9 Days CHF 2600
Applied Sciences
applsci
2.5 5.3 2011 16.9 Days CHF 2400
Machines
machines
2.1 3.0 2013 15.6 Days CHF 2400

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Published Papers (4 papers)

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16 pages, 4949 KiB  
Article
A Novel Planning and Tracking Approach for Mobile Robotic Arm in Obstacle Environment
by Jiabin Yu, Jiguang Wu, Jiping Xu, Xiaoyi Wang, Xiaoyu Cui, Bingyi Wang and Zhiyao Zhao
Machines 2024, 12(1), 19; https://doi.org/10.3390/machines12010019 - 29 Dec 2023
Viewed by 921
Abstract
In this study, a novel planning and tracking approach is proposed for a mobile robotic arm to grab objects in an obstacle environment. First, we developed an improved APF-RRT* algorithm for the motion planning of a mobile robotic arm. This algorithm optimizes the [...] Read more.
In this study, a novel planning and tracking approach is proposed for a mobile robotic arm to grab objects in an obstacle environment. First, we developed an improved APF-RRT* algorithm for the motion planning of a mobile robotic arm. This algorithm optimizes the selection of random tree nodes and smoothing the path. The invalid branch and the planning time are decreased by the artificial potential field, which is determined by the specific characteristics of obstacles. Second, a Fuzzy-DDPG-PID controller is established for the mobile robotic arm to track the planned path. The parameters of the PID controller are set using the new DDPG algorithm, which integrated FNN. The response speed and control accuracy of the controller are enhanced. The error and time of tracking of the mobile robotic arm are decreased. The experiment results verify that the proposed approach has good planning and tracking results, high speed and accuracy, and strong robustness. To avoid the occasionality of the experiments and fully illustrate the effectiveness and generality of the proposed approach, the experiments are repeated multiple times. The experiment results demonstrate the effectiveness of the proposed approach. It outperforms existing planning and tracking approaches. Full article
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17 pages, 988 KiB  
Article
Temperature Control of a Chemical Reactor Based on Neuro-Fuzzy Tuned with a Metaheuristic Technique to Improve Biodiesel Production
by Mario C. Maya-Rodriguez, Ignacio Carvajal-Mariscal, Raúl López-Muñoz, Mario A. Lopez-Pacheco and René Tolentino-Eslava
Energies 2023, 16(17), 6187; https://doi.org/10.3390/en16176187 - 25 Aug 2023
Cited by 2 | Viewed by 915
Abstract
This work deals with the problem of choosing a controller for the production of biodiesel from the transesterification process through temperature control of the chemical reactor, from the point of view of automatic control, by considering such aspects as the performance metrics based [...] Read more.
This work deals with the problem of choosing a controller for the production of biodiesel from the transesterification process through temperature control of the chemical reactor, from the point of view of automatic control, by considering such aspects as the performance metrics based on the error and the energy used by the controller, as well as the evaluation of the control system before disturbances. In addition, an improvement method is proposed via a neuro-fuzzy controller tuned with a metaheuristic algorithm to increase the efficiency of the chemical reaction in the reactor. A clear improvement is shown in the minimization of the integral of time multiplied squared error criterion (ITAE) performance index with respect to the proposed method (8.1657 ×104) in relation to the PID controller (7.8770 ×107). Moreover, the integral of the total control variation (TVU) performance index is also shown to evaluate the power used by the neuro-fuzzy controller (25.7697), while the PID controller obtains an index of (32.0287); this metric is especially relevant because it is related to the functional requirements of the system since it quantifies the variations of the control signal. Full article
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16 pages, 369 KiB  
Article
Design of a Three-Phase Shell-Type Distribution Transformer Using Evolutionary Algorithms
by Juan Carlos Olivares-Galvan, Hector Ascencion-Mestiza, Serguei Maximov, Efrén Mezura-Montes and Rafael Escarela-Perez
Energies 2023, 16(10), 4016; https://doi.org/10.3390/en16104016 - 10 May 2023
Viewed by 2121
Abstract
In this paper, three metaheuristic optimization algorithms: genetic algorithm (GA), particle swarm optimization (PSO), and differential evolution (DE) are compared in terms of minimizing the total owning cost (TOC) of the active part of a three-phase shell-type distribution transformer. The three methods use [...] Read more.
In this paper, three metaheuristic optimization algorithms: genetic algorithm (GA), particle swarm optimization (PSO), and differential evolution (DE) are compared in terms of minimizing the total owning cost (TOC) of the active part of a three-phase shell-type distribution transformer. The three methods use six inputs: power rating, primary voltage, secondary voltage, primary and secondary winding connections, and frequency. The TOC of the transformer, which includes the cost of the basic materials of the transformer plus the cost of losses, is minimized under the imposed constraints (excitation current, impedance, no-load losses, load losses, and efficiency) usually specified in the standards. As a case study, the three algorithms are applied to optimize the design of a three-phase shell-type distribution transformer of 750 kVA. All applied metaheuristic algorithms provide good results, while DE avoids local optima leading to better TOC reduction. The results of the optimization algorithms used are superior to those of the manufacturer, showing a 6% TOC reduction. Optimization of the design of a power transformer may have important implications for reducing greenhouse gas emissions and extending the lifetime of the equipment. Full article
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15 pages, 506 KiB  
Article
A Divide and Conquer Strategy for Sweeping Coverage Path Planning
by Juan Irving Vasquez and Emmanuel Alejandro Merchán-Cruz
Energies 2022, 15(21), 7898; https://doi.org/10.3390/en15217898 - 25 Oct 2022
Cited by 1 | Viewed by 1543
Abstract
One of the main challenges faced by floor treatment service robots is to compute optimal paths that completely cover a set of target areas. Short paths are of noticeable importance because their length is directly proportional to the energy used by the robot. [...] Read more.
One of the main challenges faced by floor treatment service robots is to compute optimal paths that completely cover a set of target areas. Short paths are of noticeable importance because their length is directly proportional to the energy used by the robot. Such a problem is known to be NP-hard; therefore, efficient algorithms are needed. In particular, computation efficiency is important for mobile robots with limited onboard computation capability. The general problem is known as coverage path planning (CPP). The CPP has several variants for single regions and for disjoint regions. In this research, we are investigating the solutions for disjoint target regions (rooms) that have fixed connection points (doors). In particular, we have found effective simplifications for the cases of rooms with one and two doors, while the challenging case of an unbounded number of rooms can be solved by approximation. As a result, this work presents a divide and conquer strategy (DnCS) to address the problem of finding a path for a sweeping robot that needs to sweep a set of disjoint rooms that are connected by fixed doors and corridors. The strategy divides the problem into computing the sweeping paths for the target rooms and then merges those paths into one solution by optimising the room visiting order. In this strategy, a geometrical approach for room coverage and an undirected rural postman problem optimisation are strategically combined to solve the coverage of the entire area of interest. The strategy has been tested in several synthetic maps and a real scenario showing short computation times and complete coverage. Full article
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