An adaptive optimization algorithm based on clustering analysis for return multi-flight-phase of VTVL reusable launch vehicle

Zhao, Jian; He, Xiangyue; Li, Haiyang; Lü, Lin

doi:10.1016/j.actaastro.2021.02.036

Cited by 8 publications

(4 citation statements)

References 27 publications

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“…For vertical LV landing, the problem of the reusable stage return trajectory optimizing is often considered. An example of a study on this topic can be found in the source [83]. The authors proposed an approach to automatically determine the trajectory parameters, their classification, and determination of optimal algorithms using a cluster analysis.…”

Section: Soft-landing Techniquesmentioning

confidence: 99%

Methods for the Calculation and Control of Launch Vehicle Drop Regions

et al. 2023

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The article aims at reviewing the drop regions (DR) of the launch vehicles (LV) separating parts (SP) and methods of their determination. The DRs include sea and land areas; going beyond them is associated with a number of environmental, economic, and political factors. Their combination dictates the need to ensure the safety of the people, transport, infrastructure, and environment from the negative impact of LV SPs and fuel residues. The Monte Carlo method is mostly used to determine the impact areas. It enables an estimation of the probability of the SPs of LVs falling in certain areas, constituting the DRs. These points are varied according to a set of different initial parameters. The methods of controlling the impact areas are contingently divided into engineering (based on a change in the design appearance of the LV), mathematical (which includes the changes in or optimization of the LV’s trajectory or its SP), and “soft landing” (implying the return of the LV’s to the spaceport or to a certain prepared area). The present analysis can be used as a starting point when choosing a method for determining and controlling the projected LVs and the SPs’ area of impact.

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Section: Soft-landing Techniquesmentioning

confidence: 99%

Methods for the Calculation and Control of Launch Vehicle Drop Regions

et al. 2023

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“…With recent advancements in aerospace engineering, rockets designed for single use have transitioned to systems with diverse control objectives, including maneuver control, vertical takeoff, landing, and recovery [1], [2], [3]. These systems require complex control inputs than traditional rocket controllers, which use the aerodynamic forces of the wings, attitude control thrusters, and thrust vectors of the main engines [4], [5], [6].…”

Section: Introductionmentioning

confidence: 99%

Linearized State-Space Model-Based Attitude Control for Rocket With Four Controllable Fins—Part 1-1: Basic Modeling and Identification

Kim,

Tullu,

Jung

2023

IEEE Access

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In this study, we present, analyze, and validate a linear state-space model for a simplified rocket with four fins. The model is validated experimentally using a wind tunnel. The proposed model consists of two main elements: a rocket body and fins. The body is represented as a cylinder integrated from infinitesimal sections along the longitudinal direction (x-axis), and each fin is interpreted as an individual rigid body. Applying a linear state space facilitates the use of linear analysis tools, leading to expedient system stability assessment and controller design. Stability analysis of the specific model is performed for validation. The results of the analysis showed that the phase margins of pitching and yawing were 16.9°, and that of rolling was 2.21°. However, the linearized state space carries the risk of discrepancies with the actual dynamics. The model is experimentally validated in a wind tunnel to reduce risk. The rocket model is affixed to a stationary jig in a wind tunnel and has four degrees of freedom. This arrangement allows for the verification of a coupled three-axis rotational model. The results show convergence within an average similarity of 77% in the linear range, confirming the reliability of the model. The validated model can be used for comprehensive analyses including control system design, performance optimization, and robustness analysis of a rocket with four fins in the future.

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“…Kwoh, C. K. et al proposed a new multivariate integrated clustering method by randomizing a scaling exponential similarity kernel to create a large number of diverse metrics, which are then coupled with random subspaces to form a large set of metric-subspace pairs to improve the clustering algorithm diverse metrics problem [11]. Lu, L. et al proposed an adaptive optimization algorithm for the return of multiple flight segments of a VTVL reusable launch vehicle based on clustering analysis, which solves the problem of accurately locating a soft landing in a high-dimensional parameter space with complex multivariate quantities [12].…”

Section: Introductionmentioning

confidence: 99%

“…Changhui Wang. Applied Mathematics and Nonlinear Sciences, 9(1) (2024)[1][2][3][4][5][6][7][8][9][10][11][12][13][14][15][16] …”

mentioning

confidence: 99%

Statistical method for clustering high-dimensional data based on fuzzy mathematical modeling

Wang

2023

Applied Mathematics and Nonlinear Sciences

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In this paper, firstly, the fuzzy mathematical model and its types and algorithms of fuzzy numbers are studied. Then, the concept of fuzzy mathematics is added to cluster analysis, and fuzzy cluster analysis is carried out on samples or objects after data standardization and the construction of a similarity matrix. Then, the fuzzy C-mean FCM algorithm is proposed, and the FCM algorithm is improved by introducing the point density of data objects, the maximum minimum distance method of point density sampling, and the reduction of computation to improve the statistical function of clustering high-dimensional data. Finally, the algorithm of this paper is compared with other algorithms by AC, PR, RE, convergence speed, running time, DBI index and other indexes in order to analyze the advantages of the FCM algorithm in high dimensional data clustering statistics. The results show that the F value of the FCM algorithm in the Zoo dataset reaches 0.976, which improves 61% compared to FKM and nearly 12.6% compared to the IIFKM0 algorithm, with the best clustering effect. The DBI index of the FCM algorithm is the lowest in the four datasets, which is basically stabilized in the range of 0.5-0.6. This is better than other algorithms.

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An adaptive optimization algorithm based on clustering analysis for return multi-flight-phase of VTVL reusable launch vehicle

Cited by 8 publications

References 27 publications

Methods for the Calculation and Control of Launch Vehicle Drop Regions

Methods for the Calculation and Control of Launch Vehicle Drop Regions

Linearized State-Space Model-Based Attitude Control for Rocket With Four Controllable Fins—Part 1-1: Basic Modeling and Identification

Statistical method for clustering high-dimensional data based on fuzzy mathematical modeling

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