Authors:
Maria Semenkina
1
;
Shakhnaz Akhmedova
2
;
Christina Brester
2
and
Eugene Semenkin
2
Affiliations:
1
Siberian State Aerospace University and Siberian Federal University, Russian Federation
;
2
Siberian State Aerospace University, Russian Federation
Keyword(s):
Spacecraft Command-programming Control Contours Modelling, Markov Chains, Effective Variant Choice, Multi-objective Optimization, Evolutionary Algorithms, Self-configuration, Bio-inspired Intelligence.
Related
Ontology
Subjects/Areas/Topics:
Artificial Intelligence
;
Computational Intelligence
;
Engineering Applications
;
Evolutionary Computing
;
Genetic Algorithms
;
Informatics in Control, Automation and Robotics
;
Intelligent Control Systems and Optimization
;
Optimization Algorithms
;
Robotics and Automation
;
Signal Processing, Sensors, Systems Modeling and Control
;
Soft Computing
Abstract:
Command-programming control contours of spacecraft are modelled with Markov chains. These models are used for the preliminary design of spacecraft control system effective structure. Corresponding optimization multi-objective problems with algorithmically given functions of mixed variables are solved with a special stochastic algorithms called Self-configuring Non-dominated Sorting Genetic Algorithm II, Cooperative Multi-Objective Genetic Algorithm with Parallel Implementation and Co-Operation of Biology Related Algorithms for solving multi-objective integer optimization problems which require no settings determination and parameter tuning. The high performance of the suggested algorithms is proved by solving real problems of the control contours structure preliminary design.