Application of a Six Degree of Freedom Adaptive Controller to a General Aviation Aircraft

Lemon, Kimberly

doi:10.2514/6.2011-6562

Cited by 6 publications

(3 citation statements)

References 13 publications

(19 reference statements)

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“…29,30 This research also presented an improved version of the dynamic inverse controller using all three longitudinal equations of motion and expanded to six degres-of-freedom. 31 Hinson et al 32 showed the inverse control structure with adaptation is capable of compensating for unmodeled elastic aircraft dynamics. Rafi et al 33 showed that adaptive control can be used to recover from microbursts.…”

Section: A Adaptive Control At Wichita State Universitymentioning

confidence: 98%

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Demonstration of the Optimal Control Modification for 6-DOF Control of a General Aviation Aircraft

Reed

Steck

Nguyen

2014

AIAA Guidance, Navigation, and Control Conference

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This paper presents the design of a model reference adaptive control (MRAC) system using the Optimal Control Modification (OCM) adaptation method for a general aviation aircraft. The implementation was for control of all six degrees of freedom, commanding the three aircraft angular rates and forward velocity of a Beech Bonanza, a General Aviation (GA) aircraft. The design balances the requirements of good tracking and robustness by selecting linear controller and adaptive gains that minimize the tracking error and maximizing the time delay margin. The time delay margin is critical for the GA application as the elevator actuator in the flight test aircraft has a very large (0.2 sec) delay. The designed system is shown to perform well in the nominal case and in the case of degraded control surfaces and modeling errors. Nomenclature = PI integral gain for axis u = PI proportional gain for axis u = axis placeholder, replace with p,q, or r = vector of aircraft angular rates: p,q,r = vector of pilot commands: = vector of angular rate tracking error: = vector of model angular rates: ̇ = vector of added acceleration due to adaptation: ̇ ̇ ̇ ̇ = vector of commanded accelerations: ̇ ̇ ̇ ̇ = vector of desired accelerations: ̇ ̇ ̇ ̇= vector of model accelerations: ̇ ̇ ̇ = OCM adaptiotion gain for axis u = OCM adaptive weight matrix for axis u = OCM adaptation regression vector for axis u = design damping ratio for axis u = OCM damping term adaptive gain for axis u = design frequcny for axis u

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Section: A Adaptive Control At Wichita State Universitymentioning

confidence: 98%

“…37,38 This required the modification of the inverse controller given by Lemon et al 31 to use directly the aircraft rotational accelerations as inputs, and expansion of the authors' prior OCM general aviation implementation, which was longitudinal only, to the lateral and directional axes. 37…”

Section: B Research Motivationmentioning

confidence: 99%

Demonstration of the Optimal Control Modification for 6-DOF Control of a General Aviation Aircraft

Reed

Steck

Nguyen

2014

AIAA Guidance, Navigation, and Control Conference

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“…The work has been based on the neural network based model reference adaptive controller (MRAC) work at NASA, by J.E. Steck and others [1][2][3]. The published research work were referred to aid in the development of the flight control system for the Black Kite MAV model.…”

Section: Introductionmentioning

confidence: 99%

Turbulence Effects on Modified State Observer-Based Adaptive Control: Black Kite Micro Aerial Vehicle

2016

Self Cite

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This paper presents the implementation of a modified state observer-based adaptive dynamic inverse controller for the Black Kite micro aerial vehicle. The pitch and velocity adaptations are computed by the modified state observer in the presence of turbulence to simulate atmospheric conditions. This state observer uses the estimation error to generate the adaptations and, hence, is more robust than model reference adaptive controllers which use modeling or tracking error. In prior work, a traditional proportional-integral-derivative control law was tested in simulation for its adaptive capability in the longitudinal dynamics of the Black Kite micro aerial vehicle. This controller tracks the altitude and velocity commands during normal conditions, but fails in the presence of both parameter uncertainties and system failures. The modified state observer-based adaptations, along with the proportional-integral-derivative controller enables tracking despite these conditions. To simulate flight of the micro aerial vehicle with turbulence, a Dryden turbulence model is included. The turbulence levels used are based on the absolute load factor experienced by the aircraft. The length scale was set to 2.0 meters with a turbulence intensity of 5.0 m/s that generates a moderate turbulence. Simulation results for various flight conditions show that the modified state observer-based adaptations were able to adapt to the uncertainties and the controller tracks the commanded altitude and velocity. The summary of results for all of the simulated test cases and the response plots of various states for typical flight cases are presented.

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Nguyen¹

2018

Model-Reference Adaptive Control

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Application of a Six Degree of Freedom Adaptive Controller to a General Aviation Aircraft

Cited by 6 publications

References 13 publications

Demonstration of the Optimal Control Modification for 6-DOF Control of a General Aviation Aircraft

Demonstration of the Optimal Control Modification for 6-DOF Control of a General Aviation Aircraft

Turbulence Effects on Modified State Observer-Based Adaptive Control: Black Kite Micro Aerial Vehicle

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