Analysis of Adaptive Neural Networks for Helicopter Flight Control

Leitner, Jesse; Calise, Anthony J.; Prasad, J. V. R.

doi:10.2514/2.4142

Cited by 148 publications

(41 citation statements)

References 2 publications

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“…Another method which could be used to calculate the appropriate control time histories is an inverse method using Adaptive Neural Networks (ANN). This approach has been used successfully in various rotorcraft control studies [14][15][16][17][18][19]. With this approach, the control action that is required is calculated by dynamic model inversion, which has proven to be a popular feedback linearization technique [20].…”

Section: F Propmentioning

confidence: 99%

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Maneuverability Assessment of a Compound Helicopter Configuration

Ferguson¹,

Thomson²

2016

J Am Helicopter Soc

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The compound helicopter design could potentially satisfy the new emerging requirements placed on the next generation of rotorcraft. The main benefit of the compound helicopter is its ability to reach speeds that significantly surpass the conventional helicopter. However, it is possible that the compound helicopter design can provide additional benefits in terms of maneuverability. The paper features a conventional helicopter and a compound helicopter. The conventional helicopter features a standard helicopter design with a main rotor providing the propulsive and lifting forces whereas a tail rotor, mounted at the rear of the aircraft, provides the yaw control. The compound helicopter configuration features both lift and thrust compounding. The wing offloads the main rotor at high speeds whereas two propellers provide additional axial thrust as well as yaw control. This study Presented at the 40th European Rotorcraft Forum, Southampton, U.K., September 2-5th, 2014. investigates the maneuverability of these two helicopter configurations using inverse simulation. The results predict that a compound helicopter configuration is capable of attaining greater load factors than its conventional counterpart, when flying a Pullup-Pushover maneuver. In terms of the AccelDecel maneuver, the compound helicopter configuration is able of completing the maneuver in a shorter time than the conventional helicopter, but at the expense of greater installed engine power.The addition of thrust compounding to the compound helicopter design reduces the pitch attitude required throughout the acceleration stage of the maneuver. Nomenclature

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Section: F Propmentioning

confidence: 99%

“…The end result is that there are six fifth-order piecewise continuous polynomials which are similar to Equation (19). Each of these polynomials join at their endpoints with matching displacement and slope.…”

Section: Ads-33 Pullup-pushover Maneuvermentioning

confidence: 99%

Maneuverability Assessment of a Compound Helicopter Configuration

Ferguson¹,

Thomson²

2016

J Am Helicopter Soc

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“…The update law for the neural network is based on the differential equation for the error dynamics after the dynamic; inversion is performed. The appropriate weight !update law for a PD dynamic inversion controller can be found in [5,6,7,8]. These references also include a derivation of this update law and a proof of stability of the closed-loop system with the adaptive dynamic inversion controller.…”

Section: Dvnamic Inversion Controllermentioning

confidence: 99%

Adaptive flight control using dynamic inversion and neural networks

Schumacher¹

1999

Guidance, Navigation, and Control Conference and Exhibit

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This paper presents an application of dynamic inversion control with adaptive neural networks to a tailless fighter aircraft. This technique has, in the past, typically been implemented using proportionalderivative desired dynamics, and ,has recently been extended to a special case of proportional-integral desired dynamics using implicit model-following. This paper presents a method to incorporate adaptive neural networks into an explicit model-following dynamic inversion controller with arbitrary PI desired dynamics.

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“…Many linear and nonlinear control problems have been addressed for aircrafts and UAV's like linear control [4], gain scheduling [5], Eigen structure assignment [6], dynamic inversion [7], Back stepping [8,9], direct and indirect adaptive control schemes [10][11][12][13][14], adaptive critic control [15], robust control [16][17][18] and intelligent control [11][12][13][14]. In contrast, not many control approaches have been tested for ducted fan UAV's, especially for ducted fan UAV's performing horizontal transition and forward flight.…”

Section: Introductionmentioning

confidence: 99%

Neural adaptive flight controller for ducted-fan UAV performing nonlinear maneuver

Aruneshwaran

Sundaram²,

Wang

et al. 2013

2013 IEEE Symposium on Computational Intelligence for Security and Defense Applications (CISDA)

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This paper presents a neural adaptive flight controller for ducted fan UAVs which are capable of vertical takeoff and landing(VTOL). These ducted fan propulsion systems pose great challenges in aerodynamics and control and we propose a backstepping neural adaptive control law to track its nonlinear dynamics. This controller can handle unmodeled dynamics and external disturbances as well, providing stability to the vehicle. A single layer radial basis neural network is used to approximate the unmodeled dynamics and vehicle stability is guaranteed through Lyapunov synthesis. For simulation study six degree of freedom model (6-DOF) is implemented in MATLAB along with the proposed control approach. The performance of the controller is evaluated using nonlinear bop-up maneuver and the necessary stability and tracking performance of the UAV have been investigated.Index Terms-Ducted fan, Adaptive Back stepping, Radial basis function neural networks, Lyapunov stability.

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Analysis of Adaptive Neural Networks for Helicopter Flight Control

Cited by 148 publications

References 2 publications

Maneuverability Assessment of a Compound Helicopter Configuration

Maneuverability Assessment of a Compound Helicopter Configuration

Adaptive flight control using dynamic inversion and neural networks

Neural adaptive flight controller for ducted-fan UAV performing nonlinear maneuver

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