System Identification Modeling of a Small-Scale Unmanned Rotorcraft for Flight Control Design

Mettler, Bernard; Tischler, Mark B.; Kanade, Takeo

doi:10.4050/jahs.47.50

Cited by 209 publications

(132 citation statements)

References 6 publications

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“…To reduce the complexity of the flapping model, the tip-pathplane method is applied instead of a blade element method (BEM). The representation of the main rotor tip path plane dynamics [13][14][15][16][17][18][19] is given by the following equations:…”

Section: Main Rotormentioning

confidence: 99%

Lyapunov Control Law for Automatic Approach for Unmanned Helicopter Landing

Lee

Kim

Shin

et al. 2011

Trans. Japan Soc. Aero. S Sci.

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Describing the flight behavior of a helicopter is a difficult challenge in mathematical modeling. A rotorcraft can be considered as a complex arrangement of interacting subsystems, and the problem is dominated by rotor. The rotor blades bend and twist under the influence of unsteady and nonlinear aerodynamic loads, which are themselves a function of blade motion. This problem makes it more difficult to estimate the behavior of a helicopter. Furthermore, it is difficult to design a flight controller for unmanned helicopter systems. In this paper, to obtain a nonlinear dynamic model of a helicopter, parameter identification is performed using flight test data. A globally stable tracking control law for agile and precise landing of an unmanned helicopter is proposed. A near-minimum time control scheme is adopted to design the reference trajectory, and it is shown that the control law is guaranteed to be stable globally in the sense of Lyapunov. A flight test verified the performance of the proposed method. Performance can be improved by choosing the control parameters via optimization. The proposed method can be extended to a multiple output trajectory tracking problem for a precise fixed-wing UAV landing.

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Section: Main Rotormentioning

confidence: 99%

Lyapunov Control Law for Automatic Approach for Unmanned Helicopter Landing

Lee

Kim

Shin

et al. 2011

Trans. Japan Soc. Aero. S Sci.

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“…The rate tracking loops were singleaxis proportional-integral controllers. Notch filters on cyclic commands were used to attenuate the lightly damped fuselage-rotor mode 7 and permit higher bandwidth. To ensure that these rate tracking controllers provided adequate performance, we had our pilot perform a number of axial rolls with the rate tracking loops; his strategy remained the same.…”

Section: Maneuver Execution Logicmentioning

confidence: 99%

“…However, the flapping state participates in a lightly damped fuselage-rotor mode. 7 We used a notch filter on the longitudinal cyclic to eliminate potential gain margin problems with the controller design based on the reduced-order model. 8,9 With the notch filter the attitude rate dynamics can be approximated by a first order transfer function.…”

mentioning

confidence: 99%

Control Logic for Automated Aerobatic Flight of a Miniature Helicopter

Gavrilets

Martinos

Mettler

et al. 2002

AIAA Guidance, Navigation, and Control Conference and Exhibit

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In this paper we describe the control logic that enabled a small-scale unmanned helicopter to execute a completely automatic aerobatic maneuver. The logic consists of steady-state trim trajectory controllers, used prior to, and upon the exit from the maneuver; and a maneuver logic inspired by human pilot strategies. Extensive flight tests with this control logic demonstrated smooth entry into the maneuver, automatic recovery to a steady-state trim trajectory, and the robustness of the trim-trajectory control system toward measurement and modeling errors. This approach can be extended to a variety of maneuvers.

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“…The parametric approach requires the mathematical model, order and structure of the system to be known a priori. In the parametric approach, conventional methods such as Maximum Likelihood Estimation method (MLE), Modified Maximum Likelihood Estimation method (MMLE) and Kalman Filtering (KF) [Mettler et al (2002); Jategaonkar et al (2004); Chowdhary and Jategaonkar (2006); Kallapur et al (2006)] have been employed to model the flight dynamics. In this approach, the stability and control parameters of the flight vehicle are estimated and the computed parameters are then used in the assumed aerodynamic model to model the dynamics.…”

Section: Introductionmentioning

confidence: 99%

Neural Network Based System Identification for Autonomous Flight of an Eagle Helicopter

Samal

Anavatti

Garratt

2008

IFAC Proceedings Volumes

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Neural Network Identification (NNID) for modeling the dynamics of a miniature Eagle helicopter is presented in this paper. Off-line and on-line identification is carried out for both coupled and decoupled dynamics of the helicopter from the flight test data. For both the cases, identification results and the error statistics are provided. The off-line identification performs better due to sufficient training time and data. Results indicate neural network based black-box method is suitable for modeling the nonlinear dynamics of the helicopter. This can be further applied for the design of Automatic Flight Control System (AFCS).

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System Identification Modeling of a Small-Scale Unmanned Rotorcraft for Flight Control Design

Cited by 209 publications

References 6 publications

Lyapunov Control Law for Automatic Approach for Unmanned Helicopter Landing

Lyapunov Control Law for Automatic Approach for Unmanned Helicopter Landing

Control Logic for Automated Aerobatic Flight of a Miniature Helicopter

Neural Network Based System Identification for Autonomous Flight of an Eagle Helicopter

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