Learning About Ares I from Monte Carlo Simulation

Hanson, John M.; Hall, Charlie E.

doi:10.2514/6.2008-6622

Cited by 12 publications

(3 citation statements)

References 6 publications

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“…As would be anticipated, there also exist many credible scenarios within the capability of the baseline controller in which the vehicle's ability to achieve performance targets is compromised or severely impacted. These scenarios are usually identified via extensive Monte Carlo analysis 10 . Based on the present results, significant improvements in the performance envelope can be expected with the adaptive controller as well.…”

Section: Simulation Resultsmentioning

confidence: 99%

Robust, Practical Adaptive Control for Launch Vehicles

Orr

VanZwieten

2012

AIAA Guidance, Navigation, and Control Conference

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A modern mechanization of a classical adaptive control concept is presented with an application to launch vehicle attitude control systems. Due to a rigorous flight certification environment, many adaptive control concepts are infeasible when applied to high-risk aerospace systems; methods of stability analysis are either intractable for high complexity models or cannot be reconciled in light of classical requirements. Furthermore, many adaptive techniques appearing in the literature are not suitable for application to conditionally stable systems with complex flexible-body dynamics, as is often the case with launch vehicles.The present technique is a multiplicative forward loop gain adaptive law similar to that used for the NASA X-15 flight research vehicle. In digital implementation with several novel features, it is well-suited to application on aerodynamically unstable launch vehicles with thrust vector control via augmentation of the baseline attitude/attitude-rate feedback control scheme. The approach is compatible with standard design features of autopilots for launch vehicles, including phase stabilization of lateral bending and slosh via linear filters. In addition, the method of assessing flight control stability via classical gain and phase margins is not affected under reasonable assumptions. The algorithm's ability to recover from certain unstable operating regimes can in fact be understood in terms of frequency-domain criteria. Finally, simulation results are presented that confirm the ability of the algorithm to improve performance and robustness in realistic failure scenarios.

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Section: Simulation Resultsmentioning

confidence: 99%

Robust, Practical Adaptive Control for Launch Vehicles

Orr

VanZwieten

2012

AIAA Guidance, Navigation, and Control Conference

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“…Simulating the ascent in Monte Carlo simulation with all vehicle and environmental dispersions included, for various launch times and vehicle models, provided knowledge of the statistics of vehicle attitude rates and attitude angles when there are no failures [7] [8]. Then, designing abort trigger settings to avoid false aborts (false positives) involves setting the trigger levels above the levels that are seen without failures.…”

Section: Ares I Analysis For Failures Resulting In Loss Of Controlmentioning

confidence: 99%

Launch Vehicle Abort Analysis of Failures Leading to Loss of Control

Hanson¹,

Hill²,

Beard³

2012

Journal of Spacecraft and Rockets

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“…The equivalent order statistic is thus m = N − k. For example, the tables for p A = 0.135% show that the sampling plans (k, N) = (0, 1705), (1,2880), (2,3941), and so on, all satisfy the CR = 10% constraint. Figure 9 shows the CDFs for the first two of these cases, illustrating how they provide 90% confidence that the 99.865% level of performance will be estimated.…”

Section: B From Binomial Failures To Order Statisticsmentioning

confidence: 99%

Applying Monte Carlo Simulation to Launch Vehicle Design and Requirements Verification

Hanson

Beard²

2010

AIAA Guidance, Navigation, and Control Conference

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This paper is focused on applying Monte Carlo simulation to probabilistic launch vehicle design and requirements verification. The approaches developed in this paper can be applied to other complex design efforts as well. Typically the verification must show that requirement "x" is met for at least "y"% of cases, with, say, 10% "consumer risk" or 90% confidence. Two particular aspects of making these runs for requirements verification will be explored in this paper. First, there are several types of uncertainties that should be handled in different ways, depending on when they become known (or not). The paper describes how to handle different types of uncertainties and how to develop vehicle models that can be used to examine their characteristics. This includes items that are not known exactly during the design phase but that will be known for each assembled vehicle (can be used to determine the payload capability and overall behavior of that vehicle), other items that become known before or on flight day (can be used for flight day trajectory design and go/no go decision), and items that remain unknown on flight day. Second, this paper explains a method (order statistics) for determining whether certain probabilistic requirements are met or not and enables the user to determine how many Monte Carlo samples are required. Order statistics is not new, but may not be known in general to the GN&C community. The methods also apply to determining the design values of parameters of interest in driving the vehicle design. The paper briefly discusses when it is desirable to fit a distribution to the experimental Monte Carlo results rather than using order statistics. Nomenclature Symbols= cumulative distribution function k = number of failures m = order statistic of interest, m th order statistic n = sigma level of the z vehicle target value N = number of Monte Carlo samples P BIN = binomial probability density function p = actual failure probability p = maximum likelihood estimate of p p A = allowable or acceptable failure probability (when only a consumer value is specified) p c = required failure probability for the consumer

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Learning About Ares I from Monte Carlo Simulation

Cited by 12 publications

References 6 publications

Robust, Practical Adaptive Control for Launch Vehicles

Robust, Practical Adaptive Control for Launch Vehicles

Launch Vehicle Abort Analysis of Failures Leading to Loss of Control

Applying Monte Carlo Simulation to Launch Vehicle Design and Requirements Verification

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