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Publications in Math-Net.Ru |
Citations |
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2024 |
1. |
A. I. Glushchenko, K. A. Lastochkin, “Adaptive auxiliary loop for output-based compensation of perturbations in linear systems”, Avtomat. i Telemekh., 2024, no. 9, 3–40 |
2. |
A. I. Glushchenko, K. A. Lastochkin, “Approximation-based approach to adaptive control of linear time-varying systems”, Avtomat. i Telemekh., 2024, no. 5, 86–111 |
3. |
P. Zhukov, A. I. Glushchenko, “Stability of numerical method of implicit adaptation of transient heat conduction model to rigid body thermophysical parameters”, UBS, 107 (2024), 66–87 |
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2023 |
4. |
A. I. Glushchenko, K. A. Lastochkin, “Exponentially stable adaptive control. Part iii. Time-varying plants”, Avtomat. i Telemekh., 2023, no. 11, 147–168 |
5. |
A. I. Glushchenko, K. A. Lastochkin, “Adaptive observer of state and disturbances for linear overparameterized systems”, Avtomat. i Telemekh., 2023, no. 11, 115–146 |
6. |
A. I. Glushchenko, K. A. Lastochkin, “Exponentially stable adaptive control. Part II. Switched systems”, Avtomat. i Telemekh., 2023, no. 3, 65–105 ; Autom. Remote Control, 84:3 (2023), 285–316 |
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7. |
A. I. Glushchenko, K. A. Lastochkin, “Relaxation of conditions for convergence of dynamic regressor extension and mixing procedure”, Avtomat. i Telemekh., 2023, no. 1, 23–62 ; Autom. Remote Control, 84:1 (2023), 16–47 |
8. |
P. Zhukov, A. V. Fomin, A. I. Glushchenko, “Algorithmic stability and complexity of implicit adaptation of nonstationary thermal conductivity mesh model to heated substance”, UBS, 101 (2023), 39–63 |
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9. |
A. I. Glushchenko, K. A. Lastochkin, “Constructive algorithm to vectorize $P\otimes P$ product for symmetric matrix $P$”, Zh. Vychisl. Mat. Mat. Fiz., 63:9 (2023), 1415–1427 ; Comput. Math. Math. Phys., 63:9 (2023), 1559–1570 |
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2022 |
10. |
A. I. Glushchenko, K. A. Lastochkin, V. A. Petrov, “Exponentially stable adaptive control. Part I. Time-invariant plants”, Avtomat. i Telemekh., 2022, no. 4, 62–99 ; Autom. Remote Control, 83:4 (2022), 548–578 |
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11. |
A. I. Glushchenko, K. A. Lastochkin, V. A. Petrov, “Normalization of regressor excitation in the dynamic extension and mixing procedure”, Avtomat. i Telemekh., 2022, no. 1, 22–39 ; Autom. Remote Control, 83:1 (2022), 17–31 |
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12. |
P. Zhukov, A. V. Fomin, A. I. Glushchenko, “Implicit adaptation of mesh model of transient heat conduction problem”, UBS, 100 (2022), 78–106 |
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13. |
P. Zhukov, A. I. Glushchenko, A. V. Fomin, “Comparison of finite difference and data-based models application to solve problem of steel billets temperature prediction”, UBS, 95 (2022), 79–100 |
14. |
A. V. Fomin, A. I. Glushchenko, D. A. Poleshchenko, I. S. Zorin, “Identification of steel billets marking at rolling shop no. 1 of JSC "A. A. Ugarov Oskol electrometallurgical plant" using neural networks”, UBS, 95 (2022), 62–78 |
15. |
A. I. Glushchenko, K. A. Lastochkin, V. A. Petrov, “Procedure of identification of piecewise-constant unknown parameters with improved convergence”, UBS, 95 (2022), 6–32 |
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2021 |
16. |
A. I. Glushchenko, V. A. Petrov, K. A. Lastochkin, “I-DREM: relaxing the square integrability condition”, Avtomat. i Telemekh., 2021, no. 7, 147–165 ; Autom. Remote Control, 82:7 (2021), 1233–1247 |
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17. |
A. I. Glushchenko, V. A. Petrov, K. A. Lastochkin, “Adaptive control system with a variable adjustment law gain based on the recursive least squares method”, Avtomat. i Telemekh., 2021, no. 4, 77–95 ; Autom. Remote Control, 82:4 (2021), 619–633 |
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18. |
A. I. Glushchenko, V. A. Petrov, K. A. Lastochkin, “Adaptive neural-network-based control of nonlinear underactuated plants: an example of a two-wheeled balancing robot”, Probl. Upr., 2021, no. 5, 34–47 ; Control Sciences, 2021, no. 5, 29–42 |
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2020 |
19. |
A. I. Glushchenko, V. A. Petrov, K. A. Lastochkin, “Control quality improvement of dc motor on basis of its linearization and compensation of unmodeled dynamics”, UBS, 86 (2020), 55–97 |
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2019 |
20. |
A. I. Glushchenko, “Adaptive neural network tuner of pid-controller for heating furnaces control”, Probl. Upr., 2019, no. 2, 60–69 |
21. |
A. I. Gluschenko, “On efficiency of each of pi-controller parameters adjustment with neural tuner to reject dusturbances acting on heating furnaces”, UBS, 78 (2019), 71–105 |
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2018 |
22. |
A. I. Gluschenko, “On development of neural network learning rate calculation method to adjust linear controllers online solving nonlinear plant control problem”, UBS, 72 (2018), 52–107 |
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2017 |
23. |
Y. I. Eremenko, A. I. Glushchenko, A. V. Fomin, V. A. Petrov, “PI-controller neural tuner appliance to reject disturbances acting on plants of different dynamics”, Informatsionnye Tekhnologii i Vychslitel'nye Sistemy, 2017, no. 4, 83–94 |
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2016 |
24. |
Yu. I. Eremenko, A. I. Gluschenko, “Neural network structure selection method to solve linear controllers parameters adjustment problem”, UBS, 62 (2016), 75–123 |
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2015 |
25. |
Yu. I. Eremenko, D. A. Poleshchenko, A. I. Glushchenko, “Applying neural network-based tuner to optimize parameters of PI-controller for heating furnace functioning in different modes”, UBS, 56 (2015), 143–175 |
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Presentations in Math-Net.Ru |
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Organisations |
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