Online Control Design for Learn-to-Fly

Snyder, Steven; Bacony, Barton; Morelli, Eugene A.; Frost, Susan A.; Teubert, Christopher; Okolo, Wendy

doi:10.2514/6.2018-3311

Cited by 15 publications

(3 citation statements)

References 47 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Moreover, practical factors such as sensor cost control can also easily introduce additional measurement noise. These responses change as disturbances cause changes in the dynamic parameters of the UAV system, increasing the difficulty of controller design [1][2][3]. Therefore, in the process of flight, if the aerodynamic characteristics and parameters of the UAV can be identified in real time, not only can the robustness and adaptability of the system be enhanced, but also the control accuracy can be effectively improved [4,5].…”

Section: Introductionmentioning

confidence: 99%

Time-Domain Identification Method Based on Data-Driven Intelligent Correction of Aerodynamic Parameters of Fixed-Wing UAV

Yang,

Zang,

Liu

et al. 2023

Drones

View full text Add to dashboard Cite

In order to overcome the influence of complex environmental disturbance factors such as nonlinear time-varying characteristics on the dynamic control performance of small fixed-wing UAVs, the nonlinear expression relationship of neural networks (NNs) is combined with the recursive least squares (RLSs) identification algorithm. This paper proposes a hybrid aerodynamic parameter identification method based on NN-RLS offline network training and online learning correction. The simulation results show that compared with the real value of the identification value obtained by this algorithm, the residual error of the moment coefficient is reduced by 69%, and the residual error of the force coefficient is reduced by 89%. Under the same identification accuracy, the identification time is shortened from the original 0.1 s to 0.01 s. Compared with traditional identification algorithms, better estimation results can be obtained. By using this algorithm to continuously update the NN model and iterate repeatedly, iterative learning for complex dynamic models can be realized, providing support for the optimization of UAV control schemes.

show abstract

Section: Introductionmentioning

confidence: 99%

Time-Domain Identification Method Based on Data-Driven Intelligent Correction of Aerodynamic Parameters of Fixed-Wing UAV

Yang,

Zang,

Liu

et al. 2023

Drones

View full text Add to dashboard Cite

show abstract

“…In the L2F research, the flight test aerodynamic modeling method is combined with the real-time guidance and learning control law design method on the aircraft and applied in flight. These technologies enable the aircraft to learn how to fly [21][22].…”

Section: Introductionmentioning

confidence: 99%

Flight-Propulsion Integration Dynamic Analysis and Adaptive Control of the Hypersonic Vehicle at Wide-Range Mach Numbers

et al. 2022

IEEE Access

View full text Add to dashboard Cite

There are serious among various components of the hypersonic vehicle at wide-range Mach numbers, and the coupling among aerodynamic shape, propulsion system, structure, and control system is remarkable. For excellent performance, its components and functions are highly integrated, which brings serious challenges to traditional design examples, methods, and tools. To solve the integrated control problem of flight-propulsion coupling, a controller based on pole assignment is designed in this paper, and the control parameters can be adjusted according to real-time aerodynamic data. The parametric model of hypersonic vehicle at wide-range Mach numbers with coupling characteristics is established and the flight data under various working conditions are obtained. The flight dynamical model is established with a coupling relationship between the airframe and engine. Combined with the characteristic parameters of the aircraft attitude control closed-loop system, the control gain is designed based on expected poles and adjusted in real-time. The comparative simulations are carried out, and the results show that the control gain adjustment strategy based on aerodynamic parameter identification can improve the attitude angle command tracking quality and the robustness of the system.

show abstract

“…1. References [1][2][3][4][5][6][7][8][9][10] present advances in aircraft system identification and feedback control developed during the project towards this goal. The Learn-to-Fly concept can also be applied to the related problem of selecting proper feedback control gains in a flight control system to achieve desired closed-loop performance.…”

Section: Introductionmentioning

confidence: 99%

A Learn-to-Fly Approach for Adaptively Tuning Flight Control Systems

Grauer

2018

2018 Atmospheric Flight Mechanics Conference

View full text Add to dashboard Cite

A method is presented for adaptively tuning feedback control gains in a flight control system to achieve desired closed-loop performance. The method combines efficient parameter estimation for identifying closed-loop dynamics models, with online nonlinear optimization for sequentially perturbing and updating control gains to improve performance. Prior information on stability and control derivatives is not needed, nor is any knowledge about the control system architecture. Following convergence, the optimized control gains (with uncertainties), the open-loop dynamics model, and the closed-loop dynamics model are available. The method is demonstrated for tuning a longitudinal stability augmentation system using a realistic nonlinear flight dynamics simulation of the NASA FASER airplane. Convergence was attained using five piloted maneuvers that spanned approximately one minute of flight test time. Although demonstrated for a relatively simple case, the method is general and can be applied to other aircraft, axes, performance metrics, and control systems.

show abstract

Online Control Design for Learn-to-Fly

Cited by 15 publications

References 47 publications

Time-Domain Identification Method Based on Data-Driven Intelligent Correction of Aerodynamic Parameters of Fixed-Wing UAV

Time-Domain Identification Method Based on Data-Driven Intelligent Correction of Aerodynamic Parameters of Fixed-Wing UAV

Flight-Propulsion Integration Dynamic Analysis and Adaptive Control of the Hypersonic Vehicle at Wide-Range Mach Numbers

A Learn-to-Fly Approach for Adaptively Tuning Flight Control Systems

Contact Info

Product

Resources

About