Many-objective robust trajectory optimisation under epistemic uncertainty and imprecision

Marto, Simão da Graça; Vasile, Massimiliano

doi:10.1016/j.actaastro.2021.10.022

Cited by 8 publications

(2 citation statements)

References 26 publications

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“…Multiple studies have addressed robust multi-objective trajectory optimization problems. One approach is to transcribe the probabilistic problem into a minimization of the lower expectations using surrogate modeling [25]. Furthermore, Refs.…”

Section: Introductionmentioning

confidence: 99%

Robust Constrained Multi-Objective Guidance of Supersonic Transport Landing Using Evolutionary Algorithm and Polynomial Chaos

Takubo,

Kanazaki

2023

Aerospace

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Landing of supersonic transport (SST) suffers from a large uncertainty due to its highly sensitive aerodynamic properties in the subsonic domain, as well as the wind gusts around runways. At the vehicle design stage, a landing trajectory optimization under wind uncertainty in a multi-objective solution space is desired to explore the possible trade-off in its key flight performance metrics. The proposed algorithm solves this robust constrained multi-objective optimal control problem by integrating non-intrusive polynomial chaos expansion into a constrained evolutionary algorithm. The computationally tractable optimization is made possible through the conversion of a probabilistic problem into an equivalent deterministic representation while maintaining a form of the multi-objective problem. The generated guidance trajectories achieve a significant reduction of the uncertainty in their terminal states with a marginal modification in the control history of the deterministic solutions, validating the importance of the consideration of robustness in trajectory optimization.

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Section: Introductionmentioning

confidence: 99%

Robust Constrained Multi-Objective Guidance of Supersonic Transport Landing Using Evolutionary Algorithm and Polynomial Chaos

Takubo,

Kanazaki

2023

Aerospace

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“…Approaches based on evidence theory to model epistemic uncertainty were developed for the robust optimisation of transfers under system and dynamical uncertainties [26][27][28]. A generalisation to multi-objective problems and uncertainty modelled with p-boxes was developed to compute families of control laws and tested on a rendezvous scenario [29].…”

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confidence: 99%

Robust Space Trajectory Design Using Belief Optimal Control

Greco

Campagnola

Vasile

2022

Journal of Guidance, Control, and Dynamics

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This paper presents a novel approach to the robust solution of optimal impulsive control problems under aleatory and epistemic uncertainty. The novel approach uses belief Markov decision processes to reformulate the control problem in terms of uncertainty distributions, called beliefs, rather than the realisations of the system states. This formulation leads to the definition of a Belief Optimal Control problem where the cost function and constraints are functions of the uncertainty distributions. The control formulation encompasses orbit determination arcs as well. The belief optimisation is solved with a shooting-like transcription and a nonlinear programming solver to optimise the resulting discretised problem. Both aleatory and epistemic uncertainties are propagated with a non-intrusive polynomial expansion to capture the nonlinearities of the dynamics. Finally, this new approach is applied to the robust optimisation of a flyby trajectory of the Europa Clipper mission in a scenario characterised by knowledge, execution and observation uncertainty.

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Satellite Manoeuvre Detection with Multistatic Radar

da Graça Marto,

Díaz Riofrío,

Ilioudis

et al. 2023

J Astronaut Sci

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Traditional radar sensors used for surveillance rely on monostatic radar principles. However, recently the use of remote radio frequency telescopes as bistatic receivers represents an interesting way to reuse existing facilities while providing additional information to improve tracking accuracy. In this paper we study the benefits of using such a system for the task of manoeuvre detection in satellites in LEO and MEO. We investigate the conditions in which a multistatic radar is advantageous for this purpose, and show concrete results based on simulated data. Moreover, we propose novel manoeuvre detection methods, and compare their accuracy to methods found in the literature. A more general way of assessing the accuracy of these manoeuvre detection methods is also proposed, with the aim of taking into account that the parameters of the manoeuvre that actually takes place also have an effect on the accuracy. These can be split into optimal control based methods, and statistical methods. We found the addition of multistatic radar to allow considerable improvement in the accuracy of the manoeuvre detection process, an improvement that is shown to be greater the greater the baseline, i.e., the distance of the receiver to the transmitter. Furthermore, the manoeuvre detection methods that accurately model the uncertainty in the measurements were found to be the most accurate.

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Many-objective robust trajectory optimisation under epistemic uncertainty and imprecision

Cited by 8 publications

References 26 publications

Robust Constrained Multi-Objective Guidance of Supersonic Transport Landing Using Evolutionary Algorithm and Polynomial Chaos

Robust Constrained Multi-Objective Guidance of Supersonic Transport Landing Using Evolutionary Algorithm and Polynomial Chaos

Robust Space Trajectory Design Using Belief Optimal Control

Satellite Manoeuvre Detection with Multistatic Radar

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