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NASA NTRS1994

Microgravity isolation system design: A case study

Many acceleration-sensitive, microgravity science experiments will require active vibration isolation from manned orbiters on which they will be mounted. The isolation problem, especially in the case of a tethered payload, is a complex three-dimensional one that is best suited to modern-control design methods. In this paper, extended H(sub 2) synthesis is used to design an active isolator (i.e., controller) for a realistic single-input-multiple-output (SIMO) microgravity vibration isolation problem. Complex mu-analysis methods are used to analyze the isolation system with respect to sensor, actuator, and umbilical uncertainties. The paper fully discusses the design process employed and the insights gained. This design case study provides a practical approach for isolation problems of greater complexity. Issues addressed include a physically intuitive state-space description of the system, disturbance and noise filters, filters for frequency weighting, and uncertainty models. The controlled system satisfies all the performance specifications and is robust with respect to model uncertainties.

Hampton, R. D.↗

NASA NTRS1993

Linear, multivariable robust control with a mu perspective

The structured singular value is a linear algebra tool developed to study a particular class of matrix perturbation problems arising in robust feedback control of multivariable systems. These perturbations are called linear fractional, and are a natural way to model many types of uncertainty in linear systems, including state-space parameter uncertainty, multiplicative and additive unmodeled dynamics uncertainty, and coprime factor and gap metric uncertainty. The structured singular value theory provides a natural extension of classical SISO robustness measures and concepts to MIMO systems. The structured singular value analysis, coupled with approximate synthesis methods, make it possible to study the tradeoff between performance and uncertainty that occurs in all feedback systems. In MIMO systems, the complexity of the spatial interactions in the loop gains make it difficult to heuristically quantify the tradeoffs that must occur. This paper examines the role played by the structured singular value (and its computable bounds) in answering these questions, as well as its role in the general robust, multivariable control analysis and design problem.

Packard, Andy↗

NASA NTRSDate not supplied

More Data Needed for Failure Rate Estimation, Validation, and Uncertainty Reduction

The currently planned schedule for advanced Environmental Control and Life Support System (ECLSS) development and test activities to support human exploration missions is unlikely to generate sufficient data to enable statistically-supportable, precise Orbital Replacement Unit (ORU) failure rate estimates to meet existing crew safety expectations. Accurate and precise failure rate estimates are critical for missions beyond Low Earth Orbit (LEO) because current risk mitigation approaches –namely regular resupply and rapid abort capabilities –will not be available. Safe operations will depend on mission planners’ ability to accurately forecast spares demand and efficiently provide the necessary resources. However, even after more than a decade of International Space Station (ISS) ECLSS operations, a significant amount of uncertainty remains in failure rate estimates. Uncertain or inaccurate failure rates result in increased risk and spares mass. A Bayesian estimation approach, such as the one currently implemented by the ISS Program, can reduce uncertainty by incorporating engineering judgement into failure rate estimates. However, experience on the ISS and with other complex systems shows that these prior failure rate estimates are often inaccurate. In addition, prior estimates are typically point values; some level of uncertainty must be added to convert these into probability distributions for Bayesian updating, and there are several potential methods for doing so. Due to the low rate of data collection, any inaccuracy in theseprior estimates currently hasa strong influence on the end result. This paper examines the challenges associated with failure rate estimation, validation, and uncertainty reduction in the context of ECLSS development for beyond-LEO missions. A variety of techniques for generating and updating Bayesian priors are discussed and evaluated using both real-world and simulated data. Potential solutions for improving failure rate estimation, including testing additional units, are analyzed and discussed, and a set of recommendations are provided for next-generation system development activities.

Reliability↗

NASA NTRSDate not supplied

More Data Needed for Failure Rate Estimation, Validation, and Uncertainty Reduction

Reliability↗

NASA NTRS2005

Probabilistic Analysis Techniques Applied to Complex Spacecraft Power System Modeling

Electric power system performance predictions are critical to spacecraft, such as the International Space Station (ISS), to ensure that sufficient power is available to support all the spacecraft s power needs. In the case of the ISS power system, analyses to date have been deterministic, meaning that each analysis produces a single-valued result for power capability because of the complexity and large size of the model. As a result, the deterministic ISS analyses did not account for the sensitivity of the power capability to uncertainties in model input variables. Over the last 10 years, the NASA Glenn Research Center has developed advanced, computationally fast, probabilistic analysis techniques and successfully applied them to large (thousands of nodes) complex structural analysis models. These same techniques were recently applied to large, complex ISS power system models. This new application enables probabilistic power analyses that account for input uncertainties and produce results that include variations caused by these uncertainties. Specifically, N&R Engineering, under contract to NASA, integrated these advanced probabilistic techniques with Glenn s internationally recognized ISS power system model, System Power Analysis for Capability Evaluation (SPACE).

Hojnicki, Jeffrey S.↗

NASA NTRS2019

Health Management and Prognostics for Electric Aircraft Powertrain

W and c Any air borne vehicle needs incorporating safety as key parameter of measure, and inclusion of autonomy raises the critical need for safety under autonomous operations. Management of faults and component degradation is key as complexity in autonomous operations grow over the period of time. Therefore, in addition to basic operational requirements, an autonomous electric vehicle should be able to make accurate estimates of its current system health and take the correct decisions to complete its mission successfully. Real-time safety and state-awareness tools are therefore essential for the vehicle to be able to reach its destination in a safe and successful manner. The need for safety assurance and health management capabilities is particularly relevant for aircraft electric propulsion systems, which are relatively new and with limited historical to learn. They are critical systems requiring high power density along with reliability, resilience, efficient management of weight, and operational costs. A model- based fault diagnosis and prognostics approach of complex critical systems can successfully accomplish the safety and state awareness goal for such electric propulsion systems, enabling autonomous decision making capability for safe and efficient operation. To identify critical components in the system a Qualitative Bayesian approach using FMECA is implemented. This requires the assessment of some quantities representing the state of the electric unmanned aerial systems (e-UAS), as well as look-ahead forecasts of such states during the entire flight, presented in form of safety metrics (SM). In-service data and performance data gathered from degraded components sup- ports diagnostic and prognostic methods for these systems, but this data can be difficult to obtain as weight and packaging restrictions reduce redundancy and instrumentation on-board the vehicle. Therefore, an model-based framework should be capable or operating with limited data. In addition to data scarcity, the variability of such complex critical systems re- quires the model-based framework to reason in the presence of uncertainty, such as sensor noise, and modeling imperfections. Quantification of errors and uncertainties in the measured states and quantities is therefore a fundamental step for a precise estimation of such SMs; un-modeled uncertainty may result in erroneous state assessment and un- reliable predictions of future states of e-UAVs. Typical, centralized model-based schemes suffer from inherent disadvantages such as computational complexity, single point of failure, and scalability issues, and therefore may fail in such a complex scenario. This paper presents a methodology for developing a system level diagnostics and prognostics approach using a Qualitative Bayesian FMECA approach along with a formal uncertainty management framework for an e-UAS. In this work we demonstrate the efficacy of the framework to predict effects of sub-system level degradation on vehicle operation incorporating uncertainty management to predict future behavior under different operating conditions.

Kulkarni, Chetan↗

NASA NTRS1988

Robustness margin calculation with dynamic and real parametric uncertainty

The problem of robust stability in feedback control systems with real uncertain parameters and unmodeled dynamics, where the latter takes the form of one complex block dynamic uncertainty, is considered. A robustness margin Tm is defined with respect to such model uncertainty structure and robust stability is characterized in terms of it. An algorithm to calculate Tm and the closely related multivariable stability margin km and structured singular value mu measures of robust stability is developed. It is shown that other control problems, such as robust performance for real parametric uncertainty, can also be handled in this framework.

Sideris, Athanasios↗

NASA NTRSDate not supplied

More Data Needed for Failure Rate Estimation, Validation, and Uncertainty Reduction

Current Environmental Control and Life Support System (ECLSS) development and test activities are not generating data fast enough to provide statistically-supportable precise Orbital Replacement Unit (ORU) failure rate estimates for future missions. Accurate and precise failure rate estimates are critical for missions beyond Low Earth Orbit (LEO) because current risk mitigation approaches – namely regular resupply and rapid abort capabilities – will not be available. Safe operations will depend on mission planners’ ability to accurately forecast spares demand and efficiently provide the necessary resources. However, even after more than a decade of operations on board the International Space Station (ISS), a significant amount of uncertainty remains in failure rate estimates. Uncertain or inaccurate failure rates result in increased risk and spares mass for future missions. A Bayesian failure rate estimation approach, such as the one currently implemented by the ISS Program, can help reduce uncertainty by incorporating engineering judgement into failure rate estimates. However, experience on the ISS and with other complex systems shows that these prior failure rate estimates are often inaccurate. In addition, prior failure rate estimates are typically point values; some level of uncertainty must be added to convert these into probability distributions for Bayesian updating, and there are several potential methods for doing so. Due to the low rate of data collection, these subjective (and often inaccurate) prior estimates currently have a strong influence on the end result. This paper examines the challenges associated with failure rate estimation, validation, and uncertainty reduction in the context of ECLSS development for beyond-LEO missions. A variety of techniques for generating and updating Bayesian priors are discussed and evaluated using both real-world and simulated data. Potential solutions for improving failure rate estimation, including testing additional units, are analyzed and discussed, and a set of recommendations are provided for next-generation system development activities.

Supportability↗

NASA NTRS2007

Probabilistic Reasoning for Robustness in Automated Planning

A general-purpose computer program for planning the actions of a spacecraft or other complex system has been augmented by incorporating a subprogram that reasons about uncertainties in such continuous variables as times taken to perform tasks and amounts of resources to be consumed. This subprogram computes parametric probability distributions for time and resource variables on the basis of user-supplied models of actions and resources that they consume. The current system accepts bounded Gaussian distributions over action duration and resource use. The distributions are then combined during planning to determine the net probability distribution of each resource at any time point. In addition to a full combinatoric approach, several approximations for arriving at these combined distributions are available, including maximum-likelihood and pessimistic algorithms. Each such probability distribution can then be integrated to obtain a probability that execution of the plan under consideration would violate any constraints on the resource. The key idea is to use these probabilities of conflict to score potential plans and drive a search toward planning low-risk actions. An output plan provides a balance between the user s specified averseness to risk and other measures of optimality.

Schaffer, Steven↗

NASA NTRS2018

Evaluating the Assumptions in an Empirical Jet-Surface Interaction Noise Model

A set of empirical jet-surface interaction noise models, developed for single-stream round nozzles exhausting over a simple surface in a static ambient, are evaluated for use in more realistic applications that include multi-stream nozzle systems, multi-plane surface geometries, and a flight-stream. The simple-single-stream models have several advantages when used in system-level noise studies: they are robust, they are quickly computed, and they are generally applicable to a wide range of configurations. However, these models require simplifying assumptions when applied to more complex jet exhaust systems; for example, previous work on multi-stream jets used an empirical formula to compute a single-stream equivalent jet potential core length that could be used to predict the noise using simple-single-stream jet-surface interaction models. This paper considers the effect of flight and multi-plane surfaces using a similar approach: introducing assumptions to simplify the complex system, applying the simple-single-stream models, and evaluating the uncertainty.

Jet Noise↗

NASA NTRS2018

Evaluating the Assumptions in an Empirical Jet-Surface Interaction Noise Model

A set of empirical jet-surface interaction noise models, developed for single-stream round nozzles exhausting over a simple surface in a static ambient, are evaluated for use in more realistic applications that include multi-stream nozzle systems, multi-plane surface geometries, and a flight-steam. The simple-single-stream models have several advantages when used in system-level noise studies: they are robust, they are quickly computed, and they are generally applicable to a wide range of configurations. However, these models require simplifying assumptions when applied to more complex jet exhaust systems; for example, previous work on multi-stream jets used an empirical formula to compute a single-stream equivalent jet potential core length that could be used to predict the noise using simple-single-stream jet-surface interaction models. This paper considers the effect of flight and multi-plane surfaces using a similar approach: introducing assumptions to simplify the complex system, applying the simple-single-stream models, and evaluating the uncertainty.

Jet Noise↗

NASA NTRSDate not supplied

Reduced-order Modeling for Spacecraft Thermal-structural Applications

Leveraging the speed of reduced-order models (ROMs), thermal analysis teams have previously had access to rapid optimization, sensitivity studies, rapid model correlation, and uncertainty quantification, to name a few. An advantage of the developed approach is its relative robustness, which enabled expansion to include structural models in addition to thermal models leading to both rapid thermal-structural and rapid structural-only analyses. This method was tested for a sample thermal-structural application and showed good performance. It was then applied to a complex NASA mission and showed how ROMs could be used to quickly evaluate system uncertainties. Finally, these methods were applied to a structural-only application and used to better optimize and understand the structural design.

Derek Hengeveld↗

NASA NTRS2017

Coupled Data Assimilation for Integrated Earth System Analysis and Prediction: Goals, Challenges, and Recommendations

The purpose of this report is to identify fundamental issues for coupled data assimilation (CDA), such as gaps in science and limitations in forecasting systems, in order to provide guidance to the World Meteorological Organization (WMO) on how to facilitate more rapid progress internationally. Coupled Earth system modeling provides the opportunity to extend skillful atmospheric forecasts beyond the traditional two-week barrier by extracting skill from low-frequency state components such as the land, ocean, and sea ice. More generally, coupled models are needed to support seamless prediction systems that span timescales from weather, subseasonal to seasonal (S2S), multiyear, and decadal. Therefore, initialization methods are needed for coupled Earth system models, either applied to each individual component (called Weakly Coupled Data Assimilation - WCDA) or applied the coupled Earth system model as a whole (called Strongly Coupled Data Assimilation - SCDA). Using CDA, in which model forecasts and potentially the state estimation are performed jointly, each model domain benefits from observations in other domains either directly using error covariance information known at the time of the analysis (SCDA), or indirectly through flux interactions at the model boundaries (WCDA). Because the non-atmospheric domains are generally under-observed compared to the atmosphere, CDA provides a significant advantage over single-domain analyses. Next, we provide a synopsis of goals, challenges, and recommendations to advance CDA: Goals: (a) Extend predictive skill beyond the current capability of NWP (e.g. as demonstrated by improving forecast skill scores), (b) produce physically consistent initial conditions for coupled numerical prediction systems and reanalyses (including consistent fluxes at the domain interfaces), (c) make best use of existing observations by allowing observations from each domain to influence and improve the full earth system analysis, (d) develop a robust observation-based identification and understanding of mechanisms that determine the variability of weather and climate, (e) identify critical weaknesses in coupled models and the earth observing system, (f) generate full-field estimates of unobserved or sparsely observed variables, (g) improve the estimation of the external forcings causing changes to climate, (h) transition successes from idealized CDA experiments to real-world applications. Challenges: (a) Modeling at the interfaces between interacting components of coupled Earth system models may be inadequate for estimating uncertainty or error covariances between domains, (b) current data assimilation methods may be insufficient to simultaneously analyze domains containing multiple spatiotemporal scales of interest, (c) there is no standardization of observation data or their delivery systems across domains, (d) the size and complexity of many large-scale coupled Earth system models makes it is difficult to accurately represent uncertainty due to model parameters and coupling parameters, (e) model errors lead to local biases that can transfer between the different Earth system components and lead to coupled model biases and long-term model drift, (e) information propagation across model components with different spatiotemporal scales is extremely complicated, and must be improved in current coupled modeling frameworks, (h) there is insufficient knowledge on how to represent evolving errors in non-atmospheric model components (e.g. as sea ice, land and ocean) on the timescales of NWP.

CDA↗

NASA NTRS1993

Driving a car with custom-designed fuzzy inferencing VLSI chips and boards

Vehicle control in a-priori unknown, unpredictable, and dynamic environments requires many calculational and reasoning schemes to operate on the basis of very imprecise, incomplete, or unreliable data. For such systems, in which all the uncertainties can not be engineered away, approximate reasoning may provide an alternative to the complexity and computational requirements of conventional uncertainty analysis and propagation techniques. Two types of computer boards including custom-designed VLSI chips were developed to add a fuzzy inferencing capability to real-time control systems. All inferencing rules on a chip are processed in parallel, allowing execution of the entire rule base in about 30 microseconds, and therefore, making control of 'reflex-type' of motions envisionable. The use of these boards and the approach using superposition of elemental sensor-based behaviors for the development of qualitative reasoning schemes emulating human-like navigation in a-priori unknown environments are first discussed. Then how the human-like navigation scheme implemented on one of the qualitative inferencing boards was installed on a test-bed platform to investigate two control modes for driving a car in a-priori unknown environments on the basis of sparse and imprecise sensor data is described. In the first mode, the car navigates fully autonomously, while in the second mode, the system acts as a driver's aid providing the driver with linguistic (fuzzy) commands to turn left or right and speed up or slow down depending on the obstacles perceived by the sensors. Experiments with both modes of control are described in which the system uses only three acoustic range (sonar) sensor channels to perceive the environment. Simulation results as well as indoors and outdoors experiments are presented and discussed to illustrate the feasibility and robustness of autonomous navigation and/or safety enhancing driver's aid using the new fuzzy inferencing hardware system and some human-like reasoning schemes which may include as little as six elemental behaviors embodied in fourteen qualitative rules.

Pin, Francois G.↗

NASA NTRS2005

V and V of ISHM Software for Space Exploration

NASA has established a far-reaching and long-term program for robotic and manned exploration of the solar system, beginning with missions to the moon and Mars. The Crew Transportation System (CTS), a key system for space exploration, imposes four requirements' that ISHM addresses. These requirements have a wide range of implications for V&V and certification of ISHM. There is a range of time-criticality for ISHM actions, from prognostication, which is often (but not always) non-time-critical, to time-critical state estimation and system management under off-nominal emergency conditions. These are externally imposed requirements on ISHM that are subject to V&V. - In addition, a range of techniques are needed to implement an ISHM. The approaches to ISHM are described elsewhere. These approaches range from well-understood algorithms for low-level data analysis, validation and reporting, to AI techniques for state estimation and planning. The range of techniques, and specifically the use of AI techniques such as reasoning under uncertainty and mission planning (and re-planning), implies that several V&V approaches may be required. Depending on the ISHM architecture, traditional testing approaches may be adequate for some ISHM functionality. The AI-based approaches to reasoning under uncertainty, model-based reasoning, and planning share characteristics typical of other complex software systems, but they also have characteristics that set them apart and challenge standard V&V techniques. The range of possible solutions to the overall ISHM problem impose internal challenges to V&V. The V&V challenges increase when hard real-time constraints are imposed for time-critical functionality. For example, there is an external requirement that impending catastrophic failure of the Launch Vehicle (LV) at launch time be detected and life-saving action be taken within two seconds. In this paper we outline the challenges for ISHM V&V, existing approaches and analogs in other software application areas, and possible new approaches to the V&V challenges for space exploration ISHM.

Markosian, Lawrence↗

NASA NTRS1990

Climate Impact of Solar Variability

The conference on The Climate Impact of Solar Variability, was held at Goddard Space Flight Center from April 24 to 27, 1990. In recent years they developed a renewed interest in the potential effects of increasing greenhouse gases on climate. Carbon dioxide, methane, nitrous oxide, and the chlorofluorocarbons have been increasing at rates that could significantly change climate. There is considerable uncertainty over the magnitude of this anthropogenic change. The climate system is very complex, with feedback processes that are not fully understood. Moreover, there are two sources of natural climate variability (volcanic aerosols and solar variability) added to the anthropogenic changes which may confuse our interpretation of the observed temperature record. Thus, if we could understand the climatic impact of the natural variability, it would aid our interpretation and understanding of man-made climate changes.

Schatten, Kenneth H.↗

NASA NTRS1991

Multivariable control of the Space Shuttle remote manipulator system using H2 and H(infinity) optimization

Three linear controllers are desiged to regulate the end effector of the Space Shuttle Remote Manipulator System (SRMS) operating in Position Hold Mode. In this mode of operation, jet firings of the Orbiter can be treated as disturbances while the controller tries to keep the end effector stationary in an orbiter-fixed reference frame. The three design techniques used include: the Linear Quadratic Regulator (LQR), H2 optimization, and H-infinity optimization. The nonlinear SRMS is linearized by modelling the effects of the significant nonlinearities as uncertain parameters. Each regulator design is evaluated for robust stability in light of the parametric uncertanties using both the small gain theorem with an H-infinity norm and the less conservative micro-analysis test. All three regulator designs offer significant improvement over the current system on the nominal plant. Unfortunately, even after dropping performance requirements and designing exclusively for robust stability, robust stability cannot be achieved. The SRMS suffers from lightly damped poles with real parametric uncertainties. Such a system renders the micro-analysis test, which allows for complex peturbations, too conservative.

Prakash, OM, II↗

NASA NTRS2017

A Framework for Reliability and Safety Analysis of Complex Space Missions

Long duration and complex mission scenarios are characteristics of NASA's human exploration of Mars, and will provide unprecedented challenges. Systems reliability and safety will become increasingly demanding and management of uncertainty will be increasingly important. NASA's current pioneering strategy recognizes and relies upon assurance of crew and asset safety. In this regard, flexibility to develop and innovate in the emergence of new design environments and methodologies, encompassing modeling of complex systems, is essential to meet the challenges.

Safety Analysis↗