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Journal Nonlinear World №4 for 2024 г.
Article in number:
Modeling of trajectory movement towards a goal under uncertainty
Type of article: scientific article
DOI: 10.18127/j20700970-202404-09
UDC: 004.891
Authors:

I.V. Trundaev1

1 Financial University under the Government of the Russian Federation (Moscow, Russia)
1 ivan_t98@mail.ru

Abstract:

In the special case of the problem of controlling the movement of an object towards a target in three-dimensional Euclidean space, the problem of achieving a given neighborhood of the endpoint in the absence of accurate information about the coordinates of the target is relevant. The following limitations arise in the task: after certain periods of time, the object receives the coordinates of the target with some error ε, the control object is limited in maneuver, time for movement is limited. It is necessary to find out — with which algorithm the control object will reach the specified neighborhood of the point faster, and with the same time for solving the problem — with which algorithm the object will be closer to the initially specified point.

The methods of smooth control of the object movement in three-dimensional space to the target point are proposed. The methods of effective achievement by an object of a given neighborhood of the goal are analyzed. Methods of adequate impact are based on fuzzy sets and probability theory. The orientation of the object is described in terms of quaternions. Modeling of trajectory movement towards the goal is described. A program for the implementation of movement based on the proposed methods in real time has been written.

The solution may be of interest when the information received about the target data with some error ε is not sufficient to make an adequate conclusion about the distribution parameters ε for an effective solution of the problem under the existing conditions. This may be due to a small amount of data, due to the greater variance of the error ε relative to a given target neighborhood. The proposed approaches may be interesting when the movement of the target in space is allowed.

Pages: 70-79
For citation

Trundaev I.V. Modeling of trajectory movement towards a goal under uncertainty. Nonlinear World. 2024. V. 22. № 4. P. 70–79. DOI: https://doi.org/10.18127/ j20700970-202404-09 (In Russian)

References
  1. Druzhinina O.V., Masina O.N. O podhodah k analizu ustojchivosti nelinejnyh dinamicheskih sistem s logicheskimi regulyatorami. Sovremennye informacionnye tekhnologii i IT-obrazovanie. 2017. T. 13. № 2. S. 40–49 (In Russian).
  2. Melekhin V.B., Hachumov M.V. Nechetkaya model' situacionnogo upravleniya parametrami poleta avtonomnogo bespilotnogo letatel'nogo apparata v usloviyah neopredelennosti. Mekhatronika, avtomatizaciya, upravlenie. 2021. T. 22. № 12. S. 650–659 (In Russian).
  3. Kozadaev K.V. Kvaternionnyj bazis dlya algoritma besplatformennoj inercial'noj navigacionnoj sistemy BPLA. Vestnik Polockogo gosudarstvennogo universiteta. Seriya S. Fundamental'nye nauki. 2020. № 4. S. 10–14 (In Russian).
  4. Melekhin V.B., Hachumov M.V. Planirovanie povedeniya intellektual'nogo bespilotnogo letatel'nogo apparata v nedoopredelennoj srede. Chast' I. Struktura i primenenie frejm-mikroprogramm povedeniya. Iskusstvennyj intellekt i prinyatie reshenij. 2018. № 2. S. 73–83 (In Russian).
  5. Gürsoy-Demir H., Efe M. Ö. Adaptive second order sliding mode guidance law for missile-target interception with fuzzy logic system. Proceedings of the Institution of Mechanical Engineers, Part G: Journal of Aerospace Engineering. 2023. V. 237. № 11. P. 2665-2676.
  6. Mammadova K., Hamzayev S. Motion trajectory planning of a mobile robot flying in a fuzzy environment. Scientific Collection «InterConf+». 2023. № 33 (155). P. 375–385.
  7. Li H. et al. A target damage effectiveness assessment mathematical calculation method with uncertain information based on an adaptive fuzzy neural network. International Journal of Intelligent Systems. 2023. V. 2023.
  8. Bobrow F. et al. The Cubli: modeling and nonlinear attitude control utilizing quaternions. IEEE Access. 2021. V. 9. P. 122425–122442.
  9. Tong M. et al. A model-free fuzzy adaptive trajectory tracking control algorithm based on dynamic surface control. International Journal of Advanced Robotic Systems. 2020. V. 17. № 1. P. 1729881419894417.
  10. Awad N. et al. Model predictive control with fuzzy logic switching for path tracking of autonomous vehicles. ISA transactions. 2022. V. 129. P. 193–205.
  11. Abdelwahab M. et al. Trajectory tracking of wheeled mobile robots using z-number based fuzzy logic. IEEE Access. 2020. V. 8. P. 18426–18441.
  12. Liang X. et al. Three‐dimensional trajectory tracking of an underactuated AUV based on fuzzy dynamic surface control. IET Intelligent Transport Systems. 2020. V. 14. № 5. P. 364–370.
  13. Mai T. A. et al. A combined backstepping and adaptive fuzzy PID approach for trajectory tracking of autonomous mobile robots. Journal of the Brazilian Society of Mechanical Sciences and Engineering. 2021. V. 43. P. 1–13.
  14. URL: https://github.com/air-labs/AIML (дата обращения: 20.05.2024).
Date of receipt: 25.09.2024
Approved after review: 10.10.2024
Accepted for publication: 29.10.2024