Time-Varying Lyapunov Control Laws with Enhanced Estimation of Distribution Algorithm for Low-Thrust Trajectory Design

Abolfazl Shirazi (), Harry Holt (), Roberto Armellin () and Nicola Baresi ()
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Abolfazl Shirazi: Basque Center for Applied Mathematics - BCAM
Harry Holt: University of Surrey
Roberto Armellin: University of Auckland
Nicola Baresi: University of Surrey

A chapter in Modeling and Optimization in Space Engineering, 2023, pp 377-399 from Springer

Abstract: Abstract Enhancements in evolutionary optimization techniques are rapidly growing in many aspects of engineering, specifically in astrodynamics and space trajectory optimization and design. In this chapter, the problem of optimal design of space trajectories is tackled via an enhanced optimization algorithm within the framework of Estimation of Distribution Algorithms (EDAs), incorporated with Lyapunov and Q-law feedback control methods. First, both a simple Lyapunov function and a Q-law are formulated in Classical Orbital Elements (COEs) to provide a closed-loop low-thrust trajectory profile. The weighting coefficients of these controllers are approximated with various degrees of Hermite interpolation splines. Following this model, the unknown time series of weighting coefficients are converted to unknown interpolation points. Considering the interpolation points as the decision variables, a black-box optimization problem is formed with transfer time and fuel mass as the objective functions. An enhanced EDA is proposed and utilized to find the optimal variation of weighting coefficients for minimum-time and minimum-fuel transfer trajectories. The proposed approach is applied in some trajectory optimization problems of Earth-orbiting satellites. Results show the efficiency and the effectiveness of the proposed approach in finding optimal transfer trajectories. A comparison between the Q-law and a simple Lyapunov controller is done to show the potential of the EEDA in enabling the simple Lyapunov controller to recover the finer nuances explicitly given within the analytical expressions in the Q-law.

Keywords: Global optimization; Interpolation; Q-law; Estimation of distribution algorithms; Evolutionary approach (search for similar items in EconPapers)
Date: 2023
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DOI: 10.1007/978-3-031-24812-2_14

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