Comparison of optimization approaches on linear quadratic regulator design for trajectory tracking of a quadrotor

Linear Quadratic Regulator (LQR) is one of the most prevalent methods used in the control of unmanned aerial vehicles. LQR controllers are commonly employed in the control of both linear and non-linear systems due to their advantages such as easy-to-apply and high-performance structure. However, the...

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Bibliographic Details
Published in:Evolutionary intelligence 2024, Vol.17 (5-6), p.3225-3240
Main Authors: Ata, Baris, Gencal, Mashar Cenk
Format: Article
Language:English
Subjects:
Applications of Mathematics
Artificial Intelligence
Bioinformatics
Control
Controllers
Design optimization
Engineering
Evolutionary algorithms
Evolutionary computation
Genetic algorithms
Linear quadratic regulator
Mathematical and Computational Engineering
Mechatronics
Nonlinear control
Nonlinear systems
Optimization algorithms
Particle swarm optimization
Research Paper
Robotics
Statistical Physics and Dynamical Systems
Trajectory optimization
Unmanned aerial vehicles
Unmanned helicopters
Weighting
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Summary:Linear Quadratic Regulator (LQR) is one of the most prevalent methods used in the control of unmanned aerial vehicles. LQR controllers are commonly employed in the control of both linear and non-linear systems due to their advantages such as easy-to-apply and high-performance structure. However, there is one main difficulty that plays a significant role in the manner of determining the gain for the control signal: choosing appropriate weighting matrices. The selection of these matrices that directly affect the controller performance is generally performed by trial and error, which is laborious and time-consuming. Accordingly, various optimization algorithms have been utilized to determine the weighting matrices of the LQR controller. In this paper, the weighting matrices of the designed LQR controller were obtained using Standard Genetic Algorithm, Differential Evolution, Particle Swarm Optimization, and Grey Wolf Optimization algorithms. The obtained weighting matrices of the LQR controller were tested on an unmanned aerial vehicle simulation, and the performance of optimization algorithms were presented comparatively.
ISSN:1864-5909
1864-5917
DOI:10.1007/s12065-024-00928-5