Comparative Analysis of Modern Approaches to the Aircraft Control Systems Synthesis

Аuthors

Efremov A. V.^*, Shcherbakov A. I.

Moscow Aviation Institute (National Research University), 4, Volokolamskoe shosse, Moscow, А-80, GSP-3, 125993, Russia

*e-mail: pvl@mai.ru

Abstract

Modern control systems almost completely determine characteristics of the aircraft dynamics. The increase of the onboard computer capabilities allows realizing more complex control algorithms that ensure the desired response characteristics and high level of robustness. The following techniques of regulators elaboration, based on solving the optimal control problems, predictive control (the so-called predictive regulators) are well known. However, optimal and predictive controllers require precise knowledge of the controlled object characteristics and do not always ensure the desired control quality. The presented article considers two approaches to the synthesis of controllers for the aircraft control systems:
– Reverse dynamics principle;
– Active disturbance rejection control (ADRC).
The reverse dynamics principle is widely applied in various aircraft control systems, including serial ones. Controllers based on this approach allow ensuring the controlled element dynamics close to the integrator dynamics in a wide frequency range, which is being determined by the actuators characteristics.
The ADRC principle is based on the combination of the P or PD controller and an extended state observer. Control impact is not generated based the discrepancy between the output signal measurement and set value of the input signal, but it is based on the state assessment with the extended Luenberger state observer.
To compare the approaches under consideration, mathematical modeling was performed within the framework of this article. A nonlinear model of the dynamics of a small fixed-wing aircraft weighing 39 kg is used as a controlled element dynamics.
Mathematical modeling of the response to a step input signal of an aircraft with the controllers under consideration revealed that the reverse dynamics controller ensured a fast aperiodic response. The ADRC controller ensures an acceptable characteristics of the response as well, but with a slightly longer setting time, besides there is a static error and trifle decrease in the output signal value. In addition, the modeling revealed that the controller based on the reverse dynamics principle ensured a better response characteristic compared to the ADRC controller in case where the controlled element dynamics are not known precisely.
The wind disturbance modeling demonstrated that the controller based on the reverse dynamics principle ensures more intensive wind disturbances suppression than the ADRC controller, the output signal amplitude with dynamic inversion controller application is less by ~20–25%, which confirms the high efficiency of such a controller. It should be noted herewith that the output signal returns to the reference value faster with the ADRC controller application due to the disturbance estimation by the extended observer.

Keywords:

flight control, active disturbance suppression, principle of reverse dynamics, control algorithms

References

1.  Kostygov ES. Study of methods of parametric synthesis of automatic control systems of aircraft. Sistemnyi analiz i logistika. 2025(1):46-56. (In Russ.). DOI: 10.31799/2077-5687-2025-1-46-56
2.  Kim D, Oh HS. Black-box Optimization of PID Controllers for Aircraft Maneuvering Control. International Journal of Control Automation and Systems. 2022;20(3):703-714. DOI: 10.1007/s12555-020-0915-6
3.  Apollonov DV, Bibikova KI, Shibaev MV, et al. Creation of algorithms for the automatic control system of the convertible unmanned aerial vehicle. Trudy MAI. 2022(122). (In Russ.). DOI: 10.34759/trd2022-122-23
4.  Kulikov VV. Proportional-integral controller with position compensator in automatic longitudinal flight control system. Navigatsiya i upravlenie letatel'nymi apparatami. 2019(2):70–82. (In Russ.).
5.  Panteleev AV, Yakovlev AA. Synthesis of H-Infinity Controllers in a Finite Time Interval. Modelling and Data Analysis. 2021;11(1):5–19. (In Russ.). DOI: 10.17759/mda.2021110101
6.  Ageev AM, Sizykh VN. The flight control system optimal regulators synthesis through the solution of the inverse acor problem. Scientific Bulletin of NSTU. 2014;56(3):7-22. (In Russ.).
7.  Stepaniants GA. Structure of control of unstable object, which provide minimum of the integral of aquare of control. Aerospace MAI Journal. 2012;19(4):135-140. (In Russ.).
8.  Liu J, Ji Y, Xu L, et al. Extended State Observer Based Generalized Predictive Control for Aircraft at High Angle of Attack. International Journal of Control Automation and Systems. 2024;22(10):2578–2590. DOI: 10.1007/s12555-022-0318-y
9.  Repin AI, Kashkina TI. Specifics of application of minimax operations for aircraft lateral movement control. Aerospace MAI Journal. 2018;25(1):92-98. (In Russ.).
10.  Storozhev SA, Khizhnyakov YuN. A new status regulator adapting method employing fuzzy logic. Trudy MAI. 2021(118). (In Russ.). DOI: 10.34759/trd-2021-118-16
11.  Mbikayi Z, Efremov AV, Efremov EV. Integration of the inverse dynamics with a reference model technique, and its application for the improvement of the helicopter flying qualities. IOP Conference Series: Materials Science and Engineering. 2020;868:012016. DOI: 10.1088/1757-899X/868/1/012016
12.  Kolesnikov EN, Sidoryuk ME. A combination of inverse dynamics and H∞-optimization methods in the problem of controlling the spatial motion of an aircraft. Uchenye zapiski TsAGI. 2007;38(3-4):144-157. (In Russ.).
13.  Tischler MB, Blanken CL, Cheung KK, et al. Modernized control laws for UH-60 BLACK HAWK optimization and flight-test results. Journal of Guidance, Control, and Dynamics. 2005;28(5):964-978. DOI: 10.2514/1.8021
14.  Efremov AV, Shcherbakov AI. Influence of the order of vehicle motion equations in the implementation of an adaptive flight control system with the real time identification of controlled element dynamics parameters. Izvestiya vysshikh uchebnykh zavedenii. Aviatsionnaya Tekhnika. 2023(3):33-40. (In Russ.).
15.  Steffensen R, Steinert A, Mbikayi Z, et al. Filter and sensor delay synchronization in incremental flight control laws. Aerospace Systems. 2023;6(3):85–304. DOI: 10.1007/s42401-022-00186-2
16.  Zhivov YuG, Dueling AM. Adaptive control system for the longitudinal movement of aircraft. Uchenye zapiski TsAGI. 2012;XLIII(5):91-104. (In Russ.).
17.  Yu Y, Wang H, Shao X, et al. The attitude control of UAV in carrier landing based on ADRC. IEEE Chinese Guidance, Navigation and Control Conference (CGNCC; 12–14 August 2016; Nanjing, China). pp. 832–837. DOI: 10.1109/CGNCC.2016.7828893
18.  Lechekhab TE, Manojlovic S, Stanković MR, et al. Robust error-based active disturbance rejection control of a quadrotor. Aircraft Engineering and Aerospace Technology. 2021;93(1):89–104. DOI: 10.1108/AEAT-12-2019-0266
19.  Song Z, Zhao C, Zhang H, et al. Research on Aircraft Attitude Control Method Based on Linear Active Disturbance Rejection. International Journal of Aerospace Engineering. 2022(5):1-8. DOI: 10.1155/2022/1908020
20.  Efremov AV, Ogloblin AV, Rodchenko VV, et al. The pilot as a dynamic system. Moscow: Mashinostroenie; 1992. 330 p. (In Russ.).