  Control algorithms have been proposed based on knowledge related to
nature-inspired mechanisms, including those based on the behavior of living
beings. This paper presents a review focused on major breakthroughs carried out
in the scope of applied control inspired by the gravitational attraction
between bodies. A control approach focused on Artificial Potential Fields was
identified, as well as four optimization metaheuristics: Gravitational Search
Algorithm, Black-Hole algorithm, Multi-Verse Optimizer, and Galactic Swarm
Optimization. A thorough analysis of ninety-one relevant papers was carried out
to highlight their performance and to identify the gravitational and attraction
foundations, as well as the universe laws supporting them. Included are their
standard formulations, as well as their improved, modified, hybrid, cascade,
fuzzy, chaotic and adaptive versions. Moreover, this review also deeply delves
into the impact of universe-inspired algorithms on control problems of dynamic
systems, providing an extensive list of control-related applications, and their
inherent advantages and limitations. Strong evidence suggests that
gravitation-inspired and black-hole dynamic-driven algorithms can outperform
other well-known algorithms in control engineering, even though they have not
been designed according to realistic astrophysical phenomena and formulated
according to astrophysics laws. Even so, they support future research
directions towards the development of high-sophisticated control laws inspired
by Newtonian/Einsteinian physics, such that effective control-astrophysics
bridges can be established and applied in a wide range of applications.
