Evolutionary game theory based multi-objective optimization for control allocation of over-actuated system

Date published

2019-11-25

Free to read from

Supervisor/s

Journal Title

Journal ISSN

Volume Title

Publisher

Elsevier

Department

Type

Article

ISSN

2405-8963

Format

Citation

Park O, Shin H-S, Tsourdos A. (2019) Evolutionary game theory based multi-objective optimization for control allocation of over-actuated system. IFAC-PapersOnLine, Volume 52, Issue 12, 2019, pp. 310-315

Abstract

This research presents multi-objective optimization for control allocation problem based on the Evolutionary Game Theory to solve distribution of redundant control input on the over actuated system in real-time. Optimizing the conflicting objectives, an evolutionary game theory based approach with replicator dynamics is used to find the optimal weighting using the weighted sum method. The main idea of this method is that the best strategy or dominant solution can be selected as a solution that survives among other non-dominant solutions. The Evolutionary Game Theory considers strategies as a player and investigates how these strategies can survive using replicator dynamics with payoff matrix. The numerical simulation results show the optimal weightings selected by Evolutionary Game and how the payoff has been changed in replicator dynamics.

Description

Software Description

Software Language

Github

Keywords

Multi-Objective Optimization, Weighted Sum Method, Evolutionary Game Theory, Replicator Dynamics, Evolutionary Stable Strategy, Control Allocation

DOI

Rights

Attribution-NonCommercial 4.0 International

Relationships

Relationships

Supplements

Funder/s