Flight Control Reconfiguration based on Online Physical Model Identification and Nonlinear Dynamic Inversion

Lombaerts, Thomas; Chu, Ping; Mulder, J.A.

doi:10.2514/6.2008-7435

Cited by 16 publications

(11 citation statements)

References 15 publications

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“…(iii) Validation and verification (V&V) of RFCS Although RFCS has been rigorously investigated and tested, none of RFCS has gained flight certification in both commercial and military aircraft till this day [143]. The certification of safety-critical systems requires that stability and robustness of the control algorithms should be proved under all operating conditions.…”

Section: Future Perspectivesmentioning

confidence: 99%

Survey on nonlinear reconfigurable flight control

Jiang

et al. 2013

J. of Syst. Eng. Electron.

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An overview on nonlinear reconfigurable flight control approaches that have been demonstrated in flight-test or highfidelity simulation is presented. Various approaches for reconfigurable flight control systems are considered, including nonlinear dynamic inversion, parameter identification and neural network technologies, backstepping and model predictive control approaches. The recent research work, flight tests, and potential strength and weakness of each approach are discussed objectively in order to give readers and researchers some reference. Finally, possible future directions and open problems in this area are addressed.

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Section: Future Perspectivesmentioning

confidence: 99%

Survey on nonlinear reconfigurable flight control

Jiang

et al. 2013

J. of Syst. Eng. Electron.

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“…In (6), T h is the controlled variable to track the command, ∆T hc and δ t is the control variable. Note that T h is not measurable.…”

Section: Wed26mentioning

confidence: 99%

“…Note that T h is not measurable. As (6) is not affine in δ t , the RC technique [10] is taken to compute δ t by letting ∆T hc = T hc − T h as follows,…”

Section: Step 3: Engine Control Designmentioning

confidence: 99%

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Flight control design with hierarchical dynamic inversion

2010

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Hierarchical dynamic inversion (HDI) is proposed together with newly developed variant-factor and recursive control techniques to design autonomous flight control (AFC) laws for fixed-wing aircraft. HDI is applicable to control design of some non-affine nonlinear systems to which dynamic inversion is not applicable. The AFC law designed with HDI at the nominal flight condition is applicable to other flight conditions in a wide flight envelope so that workload can be reduced significantly in AFC design compared to gain scheduling. The simulation results demonstrate that with the AFC law designed with HDI, the aircraft successfully completes the pre-scheduled maneuvering flight in a subsonic flight envelope.

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“…In order to implement autonomous flight of those UAVs, many researchers have been exploring various AFC design methods. 6,7,8,9,10,11,12,6,13,14,15,16,17 The main problem in the AFC design is to handle two kind of nonlinearities. One is the kinematical nonlinearity and the other is the dynamical nonlinearity.…”

Section: Introductionmentioning

confidence: 99%

Autonomous Flight Control Design for a Small-scale Unmanned Helicopter

Peng

Chen

Lum

2011

AIAA Guidance, Navigation, and Control Conference

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An autonomous flight control (AFC) law is designed for a small-scale unmanned helicopter with the hierarchical dynamic inversion (HDI). HDI is based on the hierarchical structural properties of a nonlinear system to partition the nonlinear system into a number of subsystems so that the dynamic inversion based methods are easily applicable to control design of these subsystems. The recursive control (RC) technique is applied in the AFC design because the attitude subsystem is non-affine nonlinear in the control variables. RC is one of the dynamic inversion based methods and is applicable to control design of a class of non-affine nonlinear systems so that there is no iterative numerical computation needed. There is no gain scheduling based technique needed in the AFC design with HDI. The designed AFC laws for the small-scale unmanned helicopter are verified in simulation on the basic 6-DOF nonlinear model of the helicopter. The simulation results demonstrate that the resulting closed-loop system from the AFC law designed with HDI can achieve better flight performances than the closed-loop system resulting from that designed with the linear quadratic regulator in a wide range of flight conditions. The simulation results also show that the closed-loop system resulting from the AFC law designed with HDI is capable of carrying out autonomous flight.Keywords: unmanned aerial vehicle, nonlinear system design method, helicopter flight control design, dynamic inversion, autonomous flight control design. NomenclatureB gb Transformation matrix from the NED frame to the body frame B ψsr Transformation matrix form the NED frame to the half NED frame B φ Transformation matrix on roll angle B θ Transformation matrix on pitch angle B ψ Transformation matrix on yaw angle B β System matrix in model of main blade flapping motion B δ Control matrix in model of main blade flapping motion C C = cos( ), represents one of φ, θ, ψ, ψ sr C d0 Drag coefficient of main blades C lmr Lift coefficient of main blades C ltr Lift coefficient of tail bladesgains, denotes subscripts F Feedback gain matrices, denotes subscripts except b F b Aerodynamic force in the body frame, N I Identity matrix with appropriate dimensions J Inertial matrix J xMoment of inertia about x-axis of the body frame, kg·m 2 J y Moment of inertia about y-axis of the body frame, kg·m 2 J z Moment of inertia about z-axis of the body frame, kg·m 2 J xz Product of inertia about x-axis and z-axis of the body frame, kg·m 2 M b Aerodynamic moment in the body frame, N·m R mr Span of main blades, m R tr Span of tail blades, m S S = sin( ), represents one of φ, θ, ψ, ψ sr T mr Thrust generated by the main blades, N T tr Thrust generated by tail blades, N T s Sampling period, s V ab Velocity with respect to air flow in the body frame, m/s V gb Velocity with respect to ground in the body frame, m/s V gg Velocity with respect to ground in the NED frame, m/s V gh Horizontal ground speed, m/s V mr An expression of a velocity on main blades, m/s V n Horizontal velocity with respect to grou...

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Flight Control Reconfiguration based on Online Physical Model Identification and Nonlinear Dynamic Inversion

Cited by 16 publications

References 15 publications

Survey on nonlinear reconfigurable flight control

Survey on nonlinear reconfigurable flight control

Flight control design with hierarchical dynamic inversion

Autonomous Flight Control Design for a Small-scale Unmanned Helicopter

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