Evolution of flight vehicle system identification

Hamel, Peter G.; Jategaonkar, R.V.

doi:10.2514/3.46898

Cited by 240 publications

(110 citation statements)

References 65 publications

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“…A few spoilers remain e¨ec-tive, but these are not su©ciently excited by the classical control system in this short time span to allow a successful identi¦cation of their individual control e©ciencies. Therefore, separate surface excitation (SSE) is needed [32,33]. In combination with the SSPE algorithm, this will provide reliable values for the primary control e©ciencies.…”

Section: Dimensionless Moment Coe©cient C Lmentioning

confidence: 99%

Model-based fault detection and identification with online aerodynamic model structure selection

Lombaerts

2013

Progress in Flight Dynamics, Guidance, Navigation, Control, Fault Detection, and Avionics

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This publication describes a recursive algorithm for the approximation of time-varying nonlinear aerodynamic models by means of a joint adaptive selection of the model structure and parameter estimation. This procedure is called adaptive recursive orthogonal least squares (AROLS) and is an extension and modi¦cation of the previously developed ROLS procedure. This algorithm is particularly useful for model-based fault detection and identi¦cation (FDI) of aerospace systems. After the failure, a completely new aerodynamic model can be elaborated recursively with respect to structure as well as parameter values. The performance of the identi¦cation algorithm is demonstrated on a simulation data set.

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Section: Dimensionless Moment Coe©cient C Lmentioning

confidence: 99%

Model-based fault detection and identification with online aerodynamic model structure selection

Lombaerts

2013

Progress in Flight Dynamics, Guidance, Navigation, Control, Fault Detection, and Avionics

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“…As shown in Figure 2, the adaptive trajectory generator processes changes in the aircraft dynamic model fed back from the systems identification module (Hamel and Jategaonkar 1995). These changes are represented in the form of numerical dynamic coefficients in an analogous format to that utilized currently by FMS.…”

Section: Adaptive Trajectory Generator For Robust Aircraft Failure Rementioning

confidence: 99%

Adaptive trajectory planning for flight management systems

Alonso-Portillo

Atkins²

2002

40th AIAA Aerospace Sciences Meeting &Amp; Exhibit

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Current Flight Management Systems (FMS) can autonomously fly an aircraft from takeoff through landing but may not provide robust operation to anomalous events. We present an adaptive trajectory planner capable of dynamically adjusting its world model and re-computing feasible flight trajectories in response to changes in aircraft performance characteristics. To demonstrate our approach, we consider the class of situations in which an emergency landing at a nearby airport is desired (or required) for safety considerations.Our system incorporates a constraint-based search engine to select and prioritize emergency landing sites, then it synthesizes a waypointbased trajectory to the best airport based on post-anomaly flight dynamics.We present an engine failure/fuel starvation case study and illustrate the utility of our approach during a simulated thrusting power failure for a B-747 over the Bay Area.

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“…Time domain identification methods are also used for LTI systems [Jategaonkar, 2006, Hamel and Jategaonkar, 1996, Klein, 1989, and common applications include the determination of aerodynamic or hydrodynamic coefficients [Sri-Jayantha and Stengel, 1988, Stalford, 1981, Yoon et al, 2004, Yoon and Rhee, 2003]. Identifying continuous time [Rao and Unbehauen, 2006] models requires the computation of signal derivatives while identifying discrete time models does not.…”

Section: Parameter Identificationmentioning

confidence: 99%

Modeling, Identification, and Control of an Unmanned Surface Vehicle

Sonnenburg

Woolsey

2013

Journal of Field Robotics

146

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This dissertation addresses the modeling, identification, and control of an automated planing vessel. To provide motion models for trajectory generation and to enable model-based control design for trajectory tracking, several experimentally identified models are compared over a wide range of speed and planing conditions for the Virginia Tech Ribcraft Unmanned Surface Vehicle. The modeling and identification objective is to determine a model which is sufficiently rich to enable effective model-based control design and trajectory optimization, sufficiently simple to allow parameter identification, and sufficiently general to describe a variety of hull forms and actuator configurations. Beginning with a 6 degree of freedom nonlinear dynamic model, several linear steering and speed models are obtained as well as a thruster model.The Ribcraft USV tracks trajectories generated with the selected maneuvering models by using a backstepping trajectory controller. A PD cascade trajectory control law is also developed and the performance of the two controllers is compared using aggressive trajectories. The backstepping control law compares favorably to the PD cascade controller. The backstepping control law is then further modified to account for nonlinear sternward dynamics and for a constant or slowly varying fluid flow. ACKNOWLEDGMENTSThis dissertation is the product of the culmination of years of research and field experimentation that required the collective abilities only found in a group of skilled researchers. There are many people that guided me through the higher level decision-making and advising process as well as the more laborious experimentation and implementation efforts. Without these people I would not have been able to finish a dissertation even approaching this quality.

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Evolution of flight vehicle system identification

Cited by 240 publications

References 65 publications

Model-based fault detection and identification with online aerodynamic model structure selection

Model-based fault detection and identification with online aerodynamic model structure selection

Adaptive trajectory planning for flight management systems

Modeling, Identification, and Control of an Unmanned Surface Vehicle

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