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

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

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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.

• 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
• sensor fusion and state estimation
• system identification

• Modeling, Simulation, Control and Optimization in Engineering with Applications in Mathematics (10 articles)