Control, Optimization, and Mathematical Modeling of Complex Systems

doi:10.3390/books978-3-0365-7641-1

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Control, Optimization, and Mathematical Modeling of Complex Systems

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June 2023

438 pages

ISBN978-3-0365-7640-4 (Hardback)
ISBN978-3-0365-7641-1 (PDF)

https://doi.org/10.3390/books978-3-0365-7641-1

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This book is a reprint of the Special Issue Control, Optimization, and Mathematical Modeling of Complex Systems that was published in

Computer Science & Mathematics

Engineering

Physical Sciences

Public Health & Healthcare

Summary

Complex systems have long been an integral part of modern life and can be encountered everywhere. Undertaking a comprehensive study of such systems is a challenging problem, one which is impossible to solve without the use of contemporary mathematical modeling techniques. Mathematical models form the basis for the optimal design and control of complex systems. The present reprint contains all the articles accepted and published in the Special Issue of Mathematics entitled "Control, Optimization, and Mathematical Modeling of Complex Systems". This Special Issue is focused on recent theoretical and computational studies of complex systems modeling, control, and optimization. The topics addressed in this Special Issue cover a wide range of areas, including numerical simulation in physical, social, and life sciences; the modeling and analysis of complex systems based on mathematical methods and AI/ML approaches; control problems in robotics; design optimization of complex systems, modeling in economics and social sciences; stochastic models in physics and engineering; mathematical models in material science; and high-performance computing for mathematical modeling. It is our hope that the scientific results presented in this reprint will serve as valuable sources of documentation and inspiration to those seeking to delve into complex systems modeling, control, and optimization and examine their wide-ranging applications.

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Keywords

optimal control problem; evolutionary computation; robotics applications; optimal control; Lyapunov stability; equilibrium point; symbolic regression; Pontryagin’s maximum principle; continuous-time Markov chains; ergodicity bounds; discrete state space; rate of convergence; logarithmic norm; interval analysis; function approximation; global optimization; convexity evaluation; overestimators; underestimators; machine learning control; general synthesis problem; symbolic regression; optimal control; evolutionary algorithm; adaptive interpolation algorithm; interval ordinary differential equations (ODEs); sparse grids; hierarchical basis; multidimensional interpolation; high dimensions; molecular dynamics modeling; randomized maximum entropy estimation; probability density functions; Lagrange multipliers; Lyapunov-type problems; implicit function; rotation of vector field; asymptotic efficiency; thermokarst lakes; forecasting; dynamical tracking target; ship towing system; relative curvature; adaptive control; discrete velocity method; lattice Boltzmann method; computational fluid dynamics; mathematical modeling; estimation; minimax techniques; pareto optimization; regression analysis; statistical uncertainty; proton exchange membrane; proton electrolyte membrane; PEM; fuel cell; PEMFC; power electronic converter; DC–DC boost converter; model predictive control; MPC; self-scalable robots; modular robots; origami structures; complex system; synergistic effect; performance indicator; structure change; soft robotics; continuum mechanisms; modeling of complex systems; kinematic model of soft robots; mathematical modeling of complex systems; non-linear models; soft robotics; soft robotic neck; tendon-driven actuators; mathematical modelling; modelling in economics; impact of the COVID-19; logistics businesses; fractional-order virus models; stuxnet virus; numerical computing; supervisory control and data acquisition systems; computer networks; lyapunov analysis; image segmentation; remote sensing; terrain identification; data synthesis; transfer learning; controllability; observability; stochastic linear systems in finite and infinite dimensional spaces; stochastic singular linear systems in finite and infinite dimensional spaces; semigroup; evolution operator; GE-semigroup; GE-evolution operator; stochastic GE-evolution operator; feature selection; finite normal mixtures; moving separation of mixtures; deep LSTM; neural network architectures; deep learning; turbulent plasma; air–sea fluxes; n/a