Novel Variable Structure Measurement System with Intelligent Components for Flight Vehicles

• Autorzy: Shen K. , Selezneva M. S. , Neusypin K. A. , Proletarsky A. V.

Identyfikatory

DOI

10.1515/mms-2017-0025

• Języki publikacji: EN

Abstrakty

EN

The paper presents a method of developing a variable structure measurement system with intelligent components for flight vehicles. In order to find a distinguishing feature of a variable structure, a numerical criterion for selecting measuring sensors is proposed by quantifying the observability of different states of the system. Based on the Peter K. Anokhin’s theory of functional systems, a mechanism of “action acceptor” is built with intelligent components, e.g. self-organization algorithms. In this mechanism, firstly, prediction models of system states are constructed using self-organization algorithms; secondly, the predicted and measured values are compared; thirdly, an optimal structure of the measurement system is finally determined based on the results of comparison. According to the results of simulation with practical data and experiments obtained during field tests, the novel developed measurement system has the properties of high-accuracy, reliable operation and fault tolerance.

Słowa kluczowe

EN

flight vehicle; variable structure measurement system; degree of observability; self-organization algorithm; integrated navigation system

• Wydawca: Komitet Metrologii i Aparatury Naukowej PAN
• Czasopismo: Metrology and Measurement Systems
• Rocznik: 2017
• Tom: Vol. 24, nr 2
• Strony: 347--356
• Opis fizyczny: Bibliogr. 14 poz., rys., wykr., wzory

Twórcy

autor

Shen K.

• Nanjing University of Science and Technology, School of Mechanical Engineering, Nanjing, China
• Bauman Moscow State Technical University, Faculty of Computer Science and Control Systems, Moscow, Russian Federation

autor

Selezneva M. S.

• Bauman Moscow State Technical University, Faculty of Computer Science and Control Systems, Moscow, Russian Federation

autor

Neusypin K. A.

• Bauman Moscow State Technical University, Faculty of Computer Science and Control Systems, Moscow, Russian Federation

autor

Proletarsky A. V.

• Bauman Moscow State Technical University, Faculty of Computer Science and Control Systems, Moscow, Russian Federation

Bibliografia

• [1] Selezneva, M.S., Neusypin, K.A. (2016). Development of a measurement complex with intelligent component. Measurement Techniques, 59(9), 916-922.
• [2] Anokhin, P.K. (1974). Biology and neurophysiology of the conditioned reflex and its role in adaptive behavior. Pergamon Press, 190-254.
• [3] Proletarsky, A.V., Shen, K., Neusypin, K.A. (2015). Intelligent control systems: Contemporary problems in theory and implementation in practice. 2015 5th International Workshop on Computer Science and Engineering: Information Processing and Control Engineering, Apr. 15-17, Moscow, 39-47.
• [4] Groves, P.D. (2013). Principles of GNSS, inertial, and multisensor integrated navigation systems. Artech House, Inc., Boston, MA, USA, 419-448.
• [5] Mirosław, Ś., Magdalena, D. (2015). Application of Kalman filter in navigation process of automated guided vehicles. Metrol. Meas. Syst., 22(3), 443-454.
• [6] Neusypin, K.A., Proletarsky, A.V., Shen, K., et al. (2014). Aircraft self-organization algorithm with redundant trend. Journal of Nanjing University of Science and Technology, 5, 602-607.
• [7] Shen, K., Neusypin, K.A., Proletarsky, A.V. (2014). On state estimation of dynamic systems by applying scalar estimation algorithms. Proceedings of 2014 IEEE Chinese Guidance, Navigation and Control Conference, Aug. 8-10, Yantai, China, 124-129.
• [8] Chen, Z. (1991). Local observability and its application to multiple measurement estimation. IEEE Transactions on Industrial Electronics, 38(6), 491-496.
• [9] Proletarsky, A.V., Nikiforov, V.M., Neusypin, K.A. (2014). Certain aspects of designing the control complex of an advanced spacecraft. Systemy I Pribory Upravleniia, 1, 5-11.
• [10] Shen, K., Proletarsky, A.V., Neusypin, K.A. (2016). Algorithms of constructing models for compensating navigation systems of unmanned aerial vehicles. 2016 International Conference on Robotics and Automation Engineering, Aug. 27-29, Jeju-Do, South Korea, 104-108.
• [11] Groves, P.D., Handley, R.J., Runnalls, A.R. (2006). Optimising the integration of terrain-referenced navigation with INS and GPS. Journal of Navigation, 59 (1), 71-89.
• [12] Won, D.H., Lee, E., Heo, M., Sung, S. Lee, J., Lee, Y.J. (2014). GNSS integration with vision-based navigation for low GNSS visibility conditions. GPS Solutions, 18(2), 177-187.
• [13] Carlson, N.A. (1990). Federated square root filter for decentralized parallel processors. IEEE Transactions on Aerospace and Electronic Systems, 26(3), 517-525.
• [14] Xing, Z.R., Xia, Y.Q. (2016). Distributed federated Kalman filter fusion over multi-sensor unreliable networked systems. IEEE Transactions on Circuits and Systems, 63(10), 1714-1725.

Uwagi

EN

This research was supported by the Programme of Introducing Talents of Discipline to Universities in P.R. China (“111 program” No. B 16025) and the Russian Foundation for Basic Research (Project No. 16-8-00522).

PL

Opracowanie ze środków MNiSW w ramach umowy 812/P-DUN/2016 na działalność upowszechniającą naukę (zadania 2017).