Performance and Robustness Metrics for Adaptive Flight Control - Available Approaches

Heise, Christian D.; Leitão, Miguel; Holzapfel, Florian

doi:10.2514/6.2013-5090

Cited by 9 publications

(3 citation statements)

References 25 publications

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“…This will be done by evaluating the estimation performance during time-domain simulations for different adaption gain values. The metrics used for the performance characterization are the Root-mean-square (RMS) [14,38] and L ∞ -norm [39][40][41] of the temporal error between the actual CE and the corrected CE model, and the cumulative moving standard deviation (CMSD) of the estimated correction factor, Ĉ [42]. The mathematical descriptions of these metrics are defined as described in Equations ( 14) until (16).…”

Section: B Lms Parameter Estimation Algorithm Designmentioning

confidence: 99%

Adaptive Incremental Nonlinear Dynamic Inversion Flight Control for Consistent Handling Qualities

Smit

Pollack

Kampen

2022

AIAA SCITECH 2022 Forum

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Section: B Lms Parameter Estimation Algorithm Designmentioning

confidence: 99%

Adaptive Incremental Nonlinear Dynamic Inversion Flight Control for Consistent Handling Qualities

Smit

Pollack

Kampen

2022

AIAA SCITECH 2022 Forum

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“…For a full adaptive controller these bounds can be obtained from the Lyapunov stability proof. However, these analytical bounds are too conservative [180]. The performance of an adaptive controller can be analysed during and after a simulation run using various quantities, for example: The benchmark case (only baseline) in the absence of uncertainties is taken as the reference system.…”

Section: Performance Metric (Comparison Of Controllers)mentioning

confidence: 99%

“…Therefore, the energy of the deviation between the reference system and the control signal in the presence of uncertainties is defined as a normalised metric for the control signal [180]. This metric is calculated as follows:…”

Section: Performance Metric (Comparison Of Controllers)mentioning

confidence: 99%

L1 adaptive control augmentation for a hypersonic glider

Banerjee¹

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ii ABSTRACTThe aim of this research is to investigate control strategies to carry out a pull out of a ballistic trajectory (initially the same as the sub-orbital ballistic HyShot and HIFiRE trajectories) and a pulsed roll angle bank manoeuvre for a hypersonic glider. This study investigates the performance of two different control methodologies in the presence of aerodynamic, gravimetric and actuator uncertainties: pole placement control (PPC) (as the baseline) and adaptive control (to augment the PPC).Through simulations, it is shown that the PPC carries out the pull up manoeuvre (by tracking the flight path angle, ) on a scaled Generic Hypersonic Aerodynamics Model Example (GHAME model). Once the pull-up manoeuvre is carried out the lateral/directional PPCs perform satisfactorily in tracking the commanded roll angle, and maintaining a sideslip angle, , of zero degrees. The PPCs presented are all SingleInput Single Output (SISO) controllers. Pole-zero plots are utilised to highlight the stability properties of the PPC. All the controllers are stable. The differences in the performance and robustness for the various uncertainty cases are highlighted through the tracking error norm and the time delay margin (TDM). The performance of the PPC significantly worsens in the presence of uncertainties. This deterioration is quantified using tracking error norms, error dynamics acceleration and a control surface metric.adaptive control is employed as a control strategy to augment the PPC as it allows the decoupling of the control and the estimation loop. The control augmentation design is employed for both the longitudinal and the lateral/directional channels in the presence of matched and unmatched uncertainties. A piecewise constant adaptive law (to estimate the uncertainties) is adopted for all the channels. An additional challenge present in this research is that the flight path angle dynamics of the system display non-minimum phase behaviour. The control theory requires the inversion of the system dynamics, which renders the control law, which cancels the unmatched uncertainties, unstable. An inverse ABSTRACT iii DC gain method is presented and tested. Using this modification, the inversion of the system dynamics is avoided. The main step forward that has been taken in this thesis is the extension of the application of theory and its application to a non-minimum phase state feedback Linear Time Varying (LTV) systems. This modified law prevents a reformulation of the control problem, for example computing an alternative representation of the state estimator in the augmented controller in order to have all the uncertainties come in through the matched channel of the system or having a virtual inertial measurement unit (IMU) in order to make the measured values minimum phase. The augmented controller is able to cancel the uncertainties and is able to restore the performance of the controller and bring the system closer to the desired system performance. The improvement provided by the augmented controller is d...

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Region of attraction Analysis for Adaptive Control of Wing Rock System

Ignatyev

Tsourdos

et al. 2021

IFAC-PapersOnLine

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Performance and Robustness Metrics for Adaptive Flight Control - Available Approaches

Cited by 9 publications

References 25 publications

Adaptive Incremental Nonlinear Dynamic Inversion Flight Control for Consistent Handling Qualities

Adaptive Incremental Nonlinear Dynamic Inversion Flight Control for Consistent Handling Qualities

L1 adaptive control augmentation for a hypersonic glider

Region of attraction Analysis for Adaptive Control of Wing Rock System

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