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Abstract

In order to address the control-related issues in spin recovery, such as saturation problem, altitude loss, and recovery time, this work focuses on the application of nonlinear model predictive control (NMPC) technique to spin recovery. It discusses the design of NMPC, fundamental tenets of NMPC and lists its primary advantages for spin recovery. To illustrate the proposed research, the F-18 High Alpha Research Vehicle (HARV) is taken into consideration. The purpose of the current study is to determine whether the NMPC can reduce the negative effects of aerodynamic control surface saturation on spin recovery. The same is confirmed when the findings of the NMPC recovery are placed on the performance envelope of the aircraft under consideration. It has been demonstrated that NMPC lowers the overall control effort needed for spin recovery. Additionally, it is established through closed-loop simulation that the NMPC minimizes altitude loss and cuts down time needed for recovery. The aforementioned claim is backed by a comparison of the findings with a typical robust nonlinear controller, SMC designed in this paper, and results on spin recovery from earlier publications. In this way, the proposed NMPC-based control approach attempts to expand the scope of the current work.

Keywords

Flight dynamics control flat spin model predictive control sliding mode control recovery

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References

Kimberlin

. Flight testing of fixed-wing aircraft. AIAA education series. Reston, VA: AIAA, 2003, pp. 383–409.

Mason

. Stalls, spins, and safety. New York: McGraw–Hill, 1982.

Fremaux

CM.

Spin tunnel investigation of a 1/28-scale model of the NASA F-18 high alpha research Vehicle (HARV) with and without vertical tails

. NASA CR-201687, 1997, pp. 1–46.

Bennett

Lawson

. Aircraft spin analysis - theoretical predictions and comparison to flight test. AIAA aviation forum. Denver, Colorado: AIAA, 2017, pp. 1–14.

Rao

Sinha

. A sliding mode controller for aircraft simulated entry into spin. Aero Sci Technol 2013; 28: 154–163.

Rao

. Optimization of aircraft spin recovery maneuvers. Aero Sci Technol 2019; 90: 222–232.

Joseph

Chambers

Bowman

, et al.

Analysis of the flat-spin characteristics of a twin-jet swept-wing fighter airplane

. NASA TN D-5409, 1969, pp. 1–35.

DeLacerda

. Facts about spins. 2nd ed. Ames: Iowa State Univ. Press, 2002.

Landsberg

. Stall/Spin: entry point for crash and burn? Safe Pilots. Frederick, MD: Safe Skies, AOPA Air Safety Foundation, 2003, pp. 1–6.

10.

Burk

Bowman

White

. Spin-tunnel investigation of the spinning characteristics of typical single-engine general aviation airplane designs . NASA TP-1009, 1977, pp. 1–31.

11.

Tischler

Barlow

. Determination of the spin and recovery characteristics of a general aviation design. J Aircraft 1981; 18: 238–244.

12.

Bihrle

Barnhart

. Spin prediction techniques. J Aircraft 1983; 20: 97–101.

13.

Goman

Zagaynov

Khramtsovsky

. Application of bifurcation methods to nonlinear flight dynamics problems. Prog Aero Sci 1997; 33: 539–586.

14.

Carroll

Mehra

. Bifurcation analysis of nonlinear aircraft dynamics. J Guid Control Dynam 1982; 5: 529–536.

15.

Jahnke

Culick

FEC

. Application of bifurcation theory to the high-angle-of-attack dynamics of the F-14. J Aircraft 1994; 31: 26–34.

16.

Raghavendra

Sahai

Ashwani Kumar

, et al. Aircraft spin recovery, with and without thrust vectoring, using nonlinear dynamic inversion. J Aircraft 2005; 42: 1492–1503.

17.

Wankhede

Sinha

. Autorotational spin evolving toward chaos. J Aircraft 2012; 49: 1184–1189.

18.

Rao

DMKKV

Sinha

. Aircraft spin recovery using a sliding-mode controller. J Guid Control Dynam 2010; 33: 1675–1678.

19.

Bunge

Kroo

. Automatic spin recovery with minimal altitude loss. In: AIAA Guidance, Navigation, and Control Conference, 2018, Kissimmee, Florida, 8–12 January 2018, pp. 1–22.

20.

Kim

Seo

, et al. Reinforcement learning-based optimal flat spin recovery for unmanned aerial vehicle. J Guid Control Dynam 2017; 40: 1074–1081.

21.

Salahudden

DVS

Dwivedi

Giri

, et al. Aircraft flat-spin recovery using sliding-mode based attitude and altitude control. Proc Inst Mech Eng Part G J Aerosp Eng 2021; 235(8): 924.

22.

Salahudden Dwivedi

Ghosh

. Aircraft spin recovery with altitude loss reduction using closed-loop thrust control. AIAA SciTech Forum, AIAA, 2021, pp. 1–11.

23.

Salahudden

GAK

. Robust control design based aircraft flat-spin recovery using optimally deflected novel deployable fin. Aero Sci Technol 2021; 115: 106823.

24.

Salahudden

Das

Ghosh

. Sliding-mode control and strategic thrust-vectoring based aircraft flat spin recovery with altitude loss minimization. IEEE Trans Aero Electron Syst 2022; 58(4): 3271.

25.

Qin

. Badgwell. An overview of industrial model predictive control technology. In: Fifth International Conference on Chemical Process Control – CPC V. American Institute of Chemical Engineers, 1996, pp. 232–256.

26.

Qin

. Badgwell. An overview of nonlinear model predictive control applications. Nonlinear Predictive Control 2000: 369–393.

27.

Findeisen

Allgoewer

. An introduction to nonlinear model predictive control 1 principles, mathematical formulation and properties of. Control, 21st Benelux Meeting on Systems and Control 2002: 1–23.

28.

Qin

Badgwell

. A survey of industrial model predictive control technology. Control Eng Pract 2003; 11: 733–764.

29.

Magni

Scattolini

. An overview of nonlinear model predictive control. Lect Notes Control Inf Sci 2010; 402: 107.

30.

Shekhar

Kearney

Shames

. Robust model predictive control of unmanned aerial vehicles using waysets. J Guid Control Dynam 2015; 38: 1898–1907.

31.

Cunis

Liao-Mcpherson

Kolmanovsky

, et al. Model-predictive spiral and spin upset recovery control for the generic transport model simulation In: CCTA 2020 - 4th IEEE Conference on Control Technology and Applications, Montreal, Canada, 28 September 2020, pp. 1–7.

32.

Samad

. A survey on industry impact and challenges thereof. IEEE Control Syst Mag 2017; 37: 17–18.

33.

Khalil

. Nonlinear systems. 2nd ed. Upper Saddle River, NJ: Prentice–Hall, 1996.

34.

Beard

McLain

. Small unmanned aircraft: theory and practice. Princeton, NJ: Princeton University Press, 2012, pp. 1–72.

35.

Slotine

Weiping

. Applied nonlinear control. New Jersey: Prentice Hall, 1991, pp. 207–246.

36.

Rouyan

. Model simulation suitable for an aircraft at high angles of attack. MPhil. Dissertation. Bedford, UK: School of Engineering, Cranfield Univ, 2016.

Nonlinear model predictive control for aircraft flat-spin recovery

Abstract

Keywords

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References