An Overview of Evolutionary Algorithms toward Spacecraft Attitude Control

Abstract

Evolutionary algorithms can be used to solve interesting problems for aeronautical and astronautical applications, and it is a must to review the fundamentals of the most common evolutionary algorithms being used for those applications. Genetic algorithms, particle swarm optimization, firefly algorithm, ant colony optimization, artificial bee colony optimization, and the cuckoo search algorithm are presented and discussed with an emphasis on astronautical applications. In summary, the genetic algorithm and its variants can be used for a large parameter space but is more efficient in global optimization using a smaller chromosome size such that the number of parameters being optimized simultaneously is less than 1000. It is found that PID controller parameters, nonlinear parameter identification, and trajectory optimization are applications ripe for the genetic algorithm. Ant colony optimization and artificial bee colony optimization are optimization routines more suited for combinatorics, such as with trajectory optimization, path planning, scheduling, and spacecraft load bearing. Particle swarm optimization, firefly algorithm, and cuckoo search algorithms are best suited for large parameter spaces due to the decrease in computation need and function calls when compared to the genetic algorithm family of optimizers. Key areas of investigation for these social evolution algorithms are in spacecraft trajectory planning and in parameter identification.

An automated system analysis and design tool for spacecrafts

“…Other notable algorithms that belong to the category of swarm algorithms [7] are bee colony [8], cuckoo search [9], particle swarm [10] and the firefly algorithm [11][12][13]. Differences in information exchange and subsequent processing distinguish each nature-inspired approach.…”

“…GAs have further been applied to designing specific components, such as control laws [44] and requirement estimation [13] of attitude control systems. Here, it is clearly stated that the evolutionary algorithm approach is faster and more flexible than traditional approaches.…”

Metaheuristic algorithms for PID controller parameters tuning: review, approaches and open problems

“…Dorigo and others introduced a number of alterations and hybrids. Offline pheromone updates and pheromone evaporation were demonstrated in particular, which led to more widespread of ACO in 1999 (Cooper and Smeresky, 2020;Dorigo et al, 2006). In Adubi and Misra (2014), an overview of the many types of ACO algorithms, their applications, and a comparative study of a few selected versions are presented.…”

An Observer-Based Backstepping Controller for Spacecraft Attitude Control System

“…In most system models, it is impossible and/or very expensive to acquire all the states variables of the system. Examples of these systems are pollution in streams [4], jet engines [5] … etc. It is essential to reconstruct the unmeasured state variables to judge the system performance and/or to generate state feedback control strategies.…”

“…This controller is considered as an approximately timeoptimal solution to a highly constrained control scheme. A digital controller for a discrete spacecraft attitude control problem was proposed in [5] to ensure the spacecraft survivability. In [6] a survey for the suggested performance control technique was presented, with the existing research of the attitude control designs.…”