Mitigating Unfavorable Pilot Interactions with Adaptive Controllers in the Presence of Failures/Damage

Klyde, David H.; Liang, Chi-Ying; Álvarez, Daniel; Richards, Nathan D.; Adams, Richard J.; Cogan, Bruce; Dryden, Nasa

doi:10.2514/6.2011-6538

Cited by 20 publications

(18 citation statements)

References 7 publications

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“…The SAFE-Cue innovative cueing system provides force feedback to the pilot via an active control inceptor with corresponding command path gain adjustments based on a measured system error between the adaptive controller response and a nominal system response [14]. The SAFE-Cue alerts the pilot that the adaptive control system is active, provides guidance via force-feedback cues, and attenuates commands, thereby ensuring pilot-vehicle system stability and performance in the presence of damage or failures.…”

Section: A Concept Descriptionmentioning

confidence: 99%

“…A computer-based simulation of these mechanizations involved computing system error and stick velocity signals from selected tracking task runs generated in past piloted simulations through the SAFE of 20 SAFE-Cue Force and 8 SAFE-Cue gain mechanizations were selected for testing in both pitch and roll by an engineer pilot using the fixed-base simulator at Systems Technology, Inc. For the pitch axis mechanizations, adjustments were made to improve the inceptor force-feedback cues based on what was received well by pilot evaluators previously [14]. The rest of the mechanizations were tested and either eliminated quickly or noted for further testing.…”

Section: A Safe-cue Developmentmentioning

confidence: 99%

“…The original functions for the mechanization [14,16] have a linear relationship between the input and output for each structure. To add more versatility to the structures, the functions are generalized to a simple nonlinear equation in which the special case forms a linear function.…”

Section: B Safe-cue Forcementioning

confidence: 99%

“…Initial testing of the SAFE-Cue force mechanizations for roll focused on the linear case. The roll axis was not mechanized in the proof-of-concept study [14], so the mechanizations were based on past experience [17,22]. Beginning with the linear case, the mechanization parameters were tuned accordingly.…”

Section: A Safe-cue Developmentmentioning

confidence: 99%

“…The initial step in developing the SAFE-Cue mechanizations involved building from the basic linear functions resulting from the original development and testing of the SAFE-Cue concept [14]. Roughly 100 mechanizations were generated for both pitch and roll.…”

Section: A Safe-cue Developmentmentioning

confidence: 99%

See 4 more Smart Citations

Flight-Test Evaluation of a Loss-of-Control Mitigation System

Klyde

Lampton

Richards

et al. 2017

Journal of Guidance, Control, and Dynamics

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For more than a decade, researchers have been developing novel adaptive flight control systems that provide a means to safely operate an air vehicle in the presence of damage or failures. Although these systems show great promise, integration of exemplar systems in flight-test aircraft at NASA has revealed the potential for unfavorable pilot-vehicle coupling, including pilot-induced oscillations. To address this issue directly, the Smart Adaptive Flight Effective Cue system was developed that features an adaptive command path gain to mitigate oscillation tendencies and an inceptor force-feedback cue to alert the pilot that the system is active. To evaluate the system, a representative adaptive controller that had been successfully evaluated in previous flight-test programs and the Smart Adaptive Flight Effective Cue system were implemented in a Calspan Learjet in-flight simulator. Flight-test evaluations consisted of three test pilots conducting two sorties each to evaluate the effectiveness of the combined system in the presence of failures. Pilot opinion ratings and quantitative assessments of performance indicated that the Smart Adaptive Flight Effective Cue system successfully eliminated the oscillation tendencies that can lead to loss of control. Furthermore, the system restored a more linear vehicle response that allowed the pilots to successfully complete the evaluation tasks with near baseline aircraft performance. Nomenclature F cue = force-feedback pilot cue F lat = lateral stick force F s = stick force p act = aircraft roll rate p err = roll rate system error q = aircraft pitch rate q err = pitch rate system error q Nom = nominal system pitch rate δ = actual surface position δ a = aileron position δ c = commanded surface position δ lat = lateral stick position δ s = stick position δ 0 c = commanded surface position attenuated by Smart Adaptive Flight Effective Cue gain θ = aircraft pitch attitude angle τ p Θ = pitch attitude phase delay τ p Φ = roll attitude phase delay ϕ = aircraft roll attitude angle ω = frequency ω BW Φ = pitch attitude bandwidth ω BW Θ = roll attitude bandwidth ω c = crossover frequency

show abstract

Section: A Concept Descriptionmentioning

confidence: 99%

Section: A Safe-cue Developmentmentioning

confidence: 99%

Section: B Safe-cue Forcementioning

confidence: 99%

Section: A Safe-cue Developmentmentioning

confidence: 99%

Section: A Safe-cue Developmentmentioning

confidence: 99%

See 3 more Smart Citations

Flight-Test Evaluation of a Loss-of-Control Mitigation System

Klyde

Lampton

Richards

et al. 2017

Journal of Guidance, Control, and Dynamics

Self Cite

View full text Add to dashboard Cite

show abstract

The use of the Open-Loop Onset Point (OLOP) to predict rotorcraft pilot-induced oscillations

Jones

2020

CEAS Aeronaut J

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The Open-Loop Onset Point (OLOP) criterion has, for many years, been successfully used as a method to predict quasi-nonlinear pilot-induced oscillations (PIOs) for fixed-wing aircraft. Only limited research has been conducted using the criterion for prediction of PIOs occurring in rotorcraft. This paper details a study to extend the application of OLOP to rotorcraft, using the combination of control inputs appropriate for the task and a suitable pilot model. Results are compared between pilot subjective opinion and OLOP predictions, from tests performed in a ground-based simulation facility. Using 'taskspecific' application of OLOP, results obtained in the investigation are encouraging, whereby the objective predictions reflect subjective pilot assessment. From results obtained, a modified boundary is presented.

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Prediction of nonlinear pilot-induced oscillation using an intelligent human pilot model

Tan

et al. 2019

Chinese Journal of Aeronautics

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Mitigating Unfavorable Pilot Interactions with Adaptive Controllers in the Presence of Failures/Damage

Cited by 20 publications

References 7 publications

Flight-Test Evaluation of a Loss-of-Control Mitigation System

Flight-Test Evaluation of a Loss-of-Control Mitigation System

The use of the Open-Loop Onset Point (OLOP) to predict rotorcraft pilot-induced oscillations

Prediction of nonlinear pilot-induced oscillation using an intelligent human pilot model

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