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Modeling, Simulation, Control and Optimization in Engineering with Applications, 2nd...
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Modeling, Simulation, Control and Optimization in Engineering with Applications, 2nd Edition

A special issue of Mathematics (ISSN 2227-7390). This special issue belongs to the section "Engineering Mathematics".

Deadline for manuscript submissions: 31 March 2025 | Viewed by 2546

Special Issue Editors

Prof. Dr. Montserrat Gil-Martinez

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Guest Editor
Control Engineering Research Group, Electrical Engineering Department, University of La Rioja, Logroño, Spain
Interests: automatic control; control theory; robust control; quantitative feedback theory (QFT); unmanned aerial vehicles; autopilot; machine learning; wastewater control systems
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Ramón Vilanova Arbós

E-Mail Website
Guest Editor
Department of Telecommunication and System Engineering, Universitat Autonoma de Barcelona, Barcelona, Spain
Interests: wastewater control systems; PID control systems; event-based control; systems with uncertainty; analysis of control systems with several degrees of freedom; application to environmental systems
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Marian Barbu

E-Mail Website
Guest Editor
Department of Automation and Electrical Engineering, Dunarea de Jos University of Galati, Galati, Romania
Interests: wastewater control systems; control of integrated water systems; data-driven control; application to environmental systems; application to energy systems
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

The progress in information technologies, advanced programming, and computer science has significantly streamlined the application of modeling, simulation, and optimization (MSO) techniques for developing advanced control systems. This evolution has elevated MSO to a crucial stage preceding any experimental application in resolving engineering problems. Furthermore, MSO plays a pivotal role in enhancing control system design by addressing complex system dynamics, uncertainties, and constraints, thereby ensuring a confident and effective deployment process while guaranteeing a high level of compatibility with the expected control system performance.

Optimization facilitates comprehensive design approaches by accommodating realistic constraints, utilizing detailed nonlinear models of the controlled system, and addressing multi-objective problems, among others. Moreover, optimization and modeling constitute fundamental components of artificial intelligence (AI), alongside prevalent machine learning algorithms. This synergy offers promising avenues for developing automatic control systems for autonomous devices. AI algorithms can optimize control strategies, improve fault detection and diagnosis, and enable adaptive and predictive control in complex and uncertain environments. Finally, control engineering itself is a dynamic field that continuously evolves to address emerging challenges and leverage new technological advancements.

The interdisciplinary nature of modeling, simulation, optimization, and control engineering underscores a diverse range of applications across industries and biosystems, with expected profound impacts on addressing critical challenges in various domains to improve human health, environmental sustainability, and societal well-being.

Within this context, this Special Issue, as a follow-up to the successful first edition titled “Modeling, Simulation, Control and Optimization in Engineering with Applications” (https://www.mdpi.com/si/mathematics/MSCOEngineering) aims to compile a collection of case studies, examples of application, and new optimization and simulation-based techniques specifically oriented to facilitate the controller design task and ensure its successful behavior.

Topics include, but are not limited to, the following:

  • Mathematical modeling of physical systems.
  • Simulation and optimization software.
  • Computational processes in modeling, simulation, and optimization.
  • Optimization approaches for control system design.
  • Optimization and modeling in artificial intelligence.
  • Modeling, simulation, and optimization of coupled problems.
  • Modeling and simulation-based decision support systems.
  • Defining synthetic environments for engineering problems.
  • Model predictive, robust, and adaptative control.
  • Machine learning and artificial intelligence-based control systems.
  • Modeling, simulation, control, and optimization of industrial processes, electrical and energy systems, or transport systems.
  • Modeling, control, navigation, and guidance of unmanned vehicles.
  • MSCO applied to biosystems, sustainable systems, and biomedical engineering.

Prof. Dr. Montserrat Gil-Martinez
Prof. Dr. Ramón Vilanova Arbós
Prof. Dr. Marian Barbu
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Mathematics is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2600 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • control systems
  • multi-objective optimization
  • modeling
  • simulation
  • optimal control
  • robustness
  • stochastic modeling and control
  • time-varying systems
  • robust control
  • adaptative control
  • model-predictive control
  • nonlinear control
  • fuzzy systems
  • neural networks
  • numerical methods
  • fault detection
  • fault diagnosis
  • fault tolerance
  • data-driven control
  • distributed control systems
  • evolutionary computation
  • machine learning
  • signal processing
  • sensor fusion and state estimation
  • system identification

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Related Special Issue

Published Papers (3 papers)

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28 pages, 3447 KiB  
Article
Semi-Active Suspension Control Strategy Based on Negative Stiffness Characteristics
by Yanlin Chen, Shaoping Shen, Zhijie Li, Zikun Hu and Zhibin Li
Mathematics 2024, 12(21), 3346; https://doi.org/10.3390/math12213346 - 25 Oct 2024
Viewed by 474
Abstract
This paper investigates the potential of negative stiffness suspensions for enhanced vehicle vibration isolation. By analyzing and improving traditional control algorithms, we propose and experimentally validate novel skyhook, groundhook, and hybrid control strategies for suspensions with negative stiffness characteristics. We establish pavement models, [...] Read more.
This paper investigates the potential of negative stiffness suspensions for enhanced vehicle vibration isolation. By analyzing and improving traditional control algorithms, we propose and experimentally validate novel skyhook, groundhook, and hybrid control strategies for suspensions with negative stiffness characteristics. We establish pavement models, incorporate negative stiffness into suspension modeling, and develop a performance evaluation index. Our research identifies shortcomings of classical semi-active control algorithms and introduces a new band selector to combine improved control methods. Simulation results demonstrate that the proposed semi-active suspension control strategy based on negative stiffness effectively reduces body vibration and enhances vehicle ride performance. Full article
(This article belongs to the Special Issue Modeling, Simulation, Control and Optimization in Engineering with Applications, 2nd Edition)
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21 pages, 4795 KiB  
Article
Robust Leader–Follower Formation Control Using Neural Adaptive Prescribed Performance Strategies
by Fengxi Xie, Guozhen Liang and Ying-Ren Chien
Mathematics 2024, 12(20), 3259; https://doi.org/10.3390/math12203259 - 17 Oct 2024
Viewed by 656
Abstract
This paper introduces a novel leader–follower formation control strategy for autonomous vehicles, aimed at achieving precise trajectory tracking in uncertain environments. The approach is based on a graph guidance law that calculates the desired yaw angles and velocities for follower vehicles using the [...] Read more.
This paper introduces a novel leader–follower formation control strategy for autonomous vehicles, aimed at achieving precise trajectory tracking in uncertain environments. The approach is based on a graph guidance law that calculates the desired yaw angles and velocities for follower vehicles using the leader’s reference trajectory, improving system stability and predictability. A key innovation is the development of a Neural Adaptive Prescribed Performance Controller (NA-PPC), which incorporates a Radial Basis Function Neural Network (RBFNN) to approximate nonlinear system dynamics and enhances disturbance estimation accuracy. The proposed method enables high-precision trajectory tracking and formation maintenance under random disturbances, which are vital for autonomous vehicle logistics and detection technologies. Leveraging a graph-based guidance law reduces control complexity and improves robustness against external disturbances. The inclusion of second-order filters and adaptive RBFNNs further enhances nonlinear error handling, improving control performance, stability, and accuracy. The integration of guidance laws, leader–follower control strategies, backstepping techniques, and RBFNNs creates a robust formation control system capable of maintaining performance under dynamic conditions. Comprehensive computer simulations validate the effectiveness of this controller, highlighting its potential to advance autonomous vehicle formation control. Full article
(This article belongs to the Special Issue Modeling, Simulation, Control and Optimization in Engineering with Applications, 2nd Edition)
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29 pages, 8912 KiB  
Article
Collision-Free Trajectory Planning Optimization Algorithms for Two-Arm Cascade Combination System
by Jingjing Xu, Long Tao, Yanhu Pei, Qiang Cheng, Hongyan Chu and Tao Zhang
Mathematics 2024, 12(14), 2245; https://doi.org/10.3390/math12142245 - 18 Jul 2024
Viewed by 1018
Abstract
As a kind of space robot, the two-arm cascade combination system (TACCS) has been applied to perform auxiliary operations at different locations outside space cabins. The motion coupling relation of two arms and complex surrounding obstacles make the collision-free trajectory planning optimization of [...] Read more.
As a kind of space robot, the two-arm cascade combination system (TACCS) has been applied to perform auxiliary operations at different locations outside space cabins. The motion coupling relation of two arms and complex surrounding obstacles make the collision-free trajectory planning optimization of TACCS more difficult, which has become an urgent problem to be solved. For the above problem, this paper proposed collision-free and time–energy–minimum trajectory planning optimization algorithms, considering the motion coupling of two arms. In this method, the screw-based inverse kinematics (IK) model of TACCS is established to provide the basis for the motion planning in joint space by decoupling the whole IK problem into two IK sub-problems of two arms; the minimum distance calculation model is established based on the hybrid geometric enveloping way and basic distance functions, which can provide the efficient and accurate data basis for the obstacle-avoidance constraint condition of the trajectory optimization. Moreover, the single and bi-layer optimization algorithms are presented by taking motion time and energy consumption as objectives and considering obstacle-avoidance and kinematics constraints. Finally, through example cases, the results indicate that the bi-layer optimization has higher convergence efficiency under the premise of ensuring the optimization effect by separating variables and constraint terms. This work can provide theoretical and methodological support for the efficient and intelligent applications of TACCS in the space arena. Full article
(This article belongs to the Special Issue Modeling, Simulation, Control and Optimization in Engineering with Applications, 2nd Edition)
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