Advance in Control Theory and Optimization

The present reprint compiles a total of 14 articles originally published in the Special Issue "Advance in Control Theory and Optimization" of the MDPI Mathematics journal. These contributions collectively explore the latest applications of mathematical methodologies across the interconnected domains of control theory and optimization, offering both theoretical insights and practical implementations. These topics cover camera calibration; cell voltage; multiple trains systems; digital economy; reinforcement learning; multi-population model; mean-field game; distributed cooperative algorithm; library group therapy behavior; nonlinear constraints; multi-agent systems; hyperchaotic system; continuous-time linear repetitive system; iterative learning control; event-triggered control; fixed-time; constrained multiobjective optimization; high-dimensional solution space; generative adversarial network; consensus; adaptive iterative learning control; hybrid optimization; particle swarm optimization; honey badger optimization algorithm; differential evolution; robust constrained cooperative control; optimization control. The reprint is intended for a wide range of scientific subjects, including complex modeling systems, artificial intelligence, optimization and scheduling, control system analysis, and collaborative control theory. It is hoped that the reprint will be interesting and valuable for those working in the area of control and optimization, as well as for those having the proper mathematical background and willing to become familiar with recent advances of control theory and optimization.