Integrated diagnostics of rocket flight control

Gorinevsky, D.; Samar, S.; Bain, John; Aaseng, Gordon

doi:10.1109/aero.2005.1559690

Cited by 7 publications

(7 citation statements)

References 14 publications

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“…The magnitude ramp models the growth of the breach as the escaping hot gases burn an opening of increasing size in the SRM case. The augmentation force and moment are as described by (6), (11). In accordance with (5), having an augmented thrust of this magnitude means that 42% of the main engine thrust is lost because of combustion products escaping through the breach.…”

Section: Iiia Redlining -Baseline Approachmentioning

confidence: 82%

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Early Detection of Solid Rocket Motor Case-Breach Failures

Gorinevsky

Mah

Timuçin

2007

AIAA Guidance, Navigation and Control Conference and Exhibit

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This paper describes work on early detection of space launch vehicle failures that could lead to loss of control during ascent. The particular focus is on the Solid Rocket Motor (SRM) comprising First Stage of the Crew Launch Vehicle (CLV). Some of the most important failure modes leading to loss of vehicle control are caused by hot gases ejected from the SRM combustion chamber through a case breach, through a joint between SRM segments, through a nozzle joint, or through an igniter seal. These hot gases create lateral thrust augmentation, with the resulting tilting moment possibly leading to loss of control. By using Thrust Vector Control (TVC) gimballing of the main nozzle, the flight control system tries to counter any attitude disturbance, which could mask the moment augmentation till it overwhelms the TVC and the control authority is lost. The approach proposed and demonstrated in a medium-fidelity simulation of the rocket dynamics employs continuous model-based multivariable estimation and monitoring of the augmentation forces and moments for early detection of the failure. The computations are performed in two stages. First, a 6-degree-of-freedom model of vehicle dynamics is used to compute the predicted vehicle accelerations. Second, the differences with the observed accelerationsmodel prediction residuals-are used in optimal multivariable algorithms to estimate the thrust augmentation. A case breach failure may thus be detected well before its effect on trajectory becomes noticeable in the raw data.

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Section: Iiia Redlining -Baseline Approachmentioning

confidence: 82%

“…Considering axial thrust would make the model nonlinear in vector X. Combining (2), (7), (6), (11), (23), and (24) yields a linear observation model of the form (20), Y (t) = S(t)X(t), where the 4 × 4 sensitivity (fault signature) matrix has the form…”

Section: Iva Linear Gaussian Modelmentioning

confidence: 99%

Early Detection of Solid Rocket Motor Case-Breach Failures

Gorinevsky

Mah

Timuçin

2007

AIAA Guidance, Navigation and Control Conference and Exhibit

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“…The example is discussed in [9], where details of the vehicle telemetry data x(t), prediction residuals y(t), unknown faults f (t), and the relevant subsystem prediction models are given. The developed algorithms are applied to the full nonlinear models in [9] and are shown to give good estimates even in the presence of nonlinearities.…”

Section: A Rocket Ascent Examplementioning

confidence: 99%

Model predictive estimation of evolving faults

Samar¹,

Gorinevsky²

2006

2006 American Control Conference

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Abstract-In this work we present an optimization based statistical estimation approach for diagnostics in large scale systems. The fault estimation scheme relies on prediction residuals generated by detailed prediction models of the system under consideration. The system dynamics are generally nonlinear. We linearize the system around its nominal operation and estimate deviations (faults) from the nominal behavior. The statistical estimation approach is based on numerical optimization of a log-likelihood function. It allows us to estimate time varying fault parameters in an online setting, and can accommodate the loss of some sensor measurements during system operation. The proposed estimation approach is explained through examples from aerospace applications.

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“…When estimating monotonic signals, the filtering approach based on constrained optimization provides a substantial improvement over standard linear filtering methods. Some applications of optimization-based estimation based on monotonic walk models are discussed in [27], [54]. The source of monotonicity considered there is an irreversible nature of faults in the system.…”

Section: Introductionmentioning

confidence: 99%

Optimal Estimation of Deterioration From Diagnostic Image Sequence

Gorinevsky

Kim

Beard

et al. 2009

IEEE Trans. Signal Process.

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Abstract-This paper considers estimation of pixel-wise monotonic increasing (or decreasing) data from a time series of noisy blurred images. The motivation comes from estimation of mechanical structure damage that accumulates irreversibly over time. We formulate a Maximum A posteriory Probablity (MAP) estimation problem and find a solution by direct numerical optimization of a log-likelihood index. Spatial continuity of the damage is modeled using a Markov Random Field (MRF). The MRF prior includes the temporal monotonicity constraints. We tune the MRF prior, using a spatial frequency domain loopshaping technique to achieve a tradeoff between noise rejection and signal restoration properties of the estimate.The MAP optimization is a large-scale Quadratic Programming (QP) problem that could have more than a million of decision variables and constraints. We describe and implement an efficient interior-point method for solving such optimization problem. The method uses a preconditioned conjugate gradient method to compute the search step. The developed QP solver relies on the special structure of the problem and can solve the problems of this size in a few tens of minutes, on a PC.The application example in the paper describes structural damage images obtained using a Structural Health Monitoring (SHM) system. The damage signal is distorted by environmental temperature that varies for each acquired image in the series. The solution for the experimental data is demonstrated to provide an excellent estimate of the damage accumulation trend while rejecting the spatial and temporal noise.Index Terms-Optimal estimation, spatio-temporal filtering, regularization, interior-point method, isotonic regression, Markov Random Field.

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Integrated diagnostics of rocket flight control

Cited by 7 publications

References 14 publications

Early Detection of Solid Rocket Motor Case-Breach Failures

Early Detection of Solid Rocket Motor Case-Breach Failures

Model predictive estimation of evolving faults

Optimal Estimation of Deterioration From Diagnostic Image Sequence

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