Path-Adaptive Guidance Algorithm Trades for a Two-Stage Lunar Descent Vehicle
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Engineering topics
Publications and source records attributed to Jason Everett.
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National Aeronautics and Space Administration (NASA) is currently building the Space Launch System (SLS) Block-1 launch vehicle, to be used as the crewed heavy-lift vehicle for the Artemis series of missions. The SLS Block-1 guidance subsystem utilizes a nonlinear algorithm derived from Shuttle heritage for limiting the vehicle’s induced maximum axial acceleration during ascent flight, known as g-limiting. Even though g-limiting has demonstrated stability and robustness through several design analysis cycles and the extensive Shuttle flight history, there are no available documents that demonstrate that this algorithm has been proven stable through conventional controls stability analysis. This paper highlights the non-linear nature of g-limit, presents an alternative methodology to employ a linear version of this algorithm, performs assessment of selected linear gains using classical stability analysis and, conducts a comparison of both approaches through Six Degrees-of-Freedom (6-DOF) Monte Carlo (MC) simulations.
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As the design of a Mars Ascent Vehicle as part of the Mars Sample Return Campaign continues to mature, new focus is placed on the ability to reconstruct and validate the as-flown performance in post-flight analysis. This is increasingly important as the vehicle considers alternate design concepts that offer significant mass reductions at a cost of reduced onboard sensing capabilities. To understand architectural impacts to validate as-inserted vehicle conditions, the MAV team has assessed several options for capturing this data either on the vehicle or using external tracking options. This paper summarizes potential architectures, their associated performance, and integration challenges.
The objective of the Mars Ascent Vehicle (MAV), an element of the Mars Sample Return (MSR) campaign, is to successfully transport Martian surface samples from the Martian surface to a stable Low-Martian Orbit. Complete autonomy is required throughout ascent, and orbital insertion is constrained by tight dispersion boundaries. Given mass requirements to merely transport the MAV onto the Martian surface, reducing the total mass of the MAV reduces stress on all other campaign elements. An unguided, spin-stabilized second stage MAV allows for lower overall vehicle mass, at the cost of reduced GNC capability. This paper presents GNC modifications developed and employed to improve the performance of an unguided, spin-stabilized second stage MAV configuration. These updates include a predictive calculation scheme to solve the spin-stabilized attitude necessary for successful orbital insertion, and a spin-stabilized control law for a preliminary low-rate first stage spin-up. Trades were conducted focusing on first and second stage low-rate and high-rate spin rates (and other factors) in MSFC's MANTIS toolkit through 6-degree-of-freedom simulation and Monte Carlo analysis, and are presented here.
The Mars Ascent Vehicle (MAV) will be the first vehicle to perform an ascent from the surface of another atmospheric planetary body outside of the Earth-Moon system. Significant light-time delay requires complete autonomy of flight throughout ascent, and naturally a high level of reliability is desired in both MAV’s hardware and software subsystems. The MAV Guidance, Navigation and Controls (GNC) team and the MAV Flight Software (FSW) team have partnered together to improve the efficiency of algorithm integration onto the MAV flight processor, and to increase confidence that said integration is successful and without human error. An interface architecture is proposed for the GNC suite that allows both the guidance and navigation subsystems to provide code algorithms directly in C++, and the controls subsystem to provide MATLAB Simulink auto-coded algorithms. Several continuous integration/deployment (CI/CD) methodologies have been considered for ease of transition of algorithm code from the GNC team to the FSW team. The GNC/FSW teams also worked together to develop a cFS-friendly wrapper which abstracts the integration of the GNC algorithm code into an interface-level API that is compatible with cFS. Several iterations of vehicle GNC code have been produced between the GNC/FSW team’s partnership, and this strong interface between these two teams have allowed the GNC/FSW teams to greatly increase confidence of efficient and error-free implementation of the GNC code onto MAV for a successful flight.
Solar Cruiser is a solar sailing mission, riding as a secondary payload to the Interstellar Mapping and Acceleration Probe (IMAP) mission, slated to launch in February of 2025. The Solar Cruiser vehicle will generate thrust via a complex, low-thrust solar sail. The extreme low-thrust nature of the solar sail will leave Solar Cruiser highly sensitive to external environmental effects (such as solar radiation pressure and high-order gravitational perturbations) throughout the entirety of flight. Because of this, preliminary operational optimization routines must be intricately tied to high-order predictive propagation models to ensure the greatest possible confidence in mission success. The Solar Cruiser Mission Design and Navigation (MDNav) team has designed a software tool suite, employing the latest in software containerization technology, to accomplish this task; allowing for seamless development across several users and operating systems. Combining JPL’s Monte toolkit with high-performance optimizers written by the University of Alabama, the proposed architecture allows for instant verification of optimized trajectories within the same development environment that the optimization takes place, removing the need for mission designers and navigators to switch between tools. The MDNav suite image deployment process is separated from the development and operational scripts to be used in flight, which allows for maintaining a low-footprint version control profile without unnecessary file bloating. This paper discusses the historical differences between previous iterations of the Solar Cruiser MDNav tool suite and the current iteration, planned operational interfaces of the tool with other software and subsystems, and the planned path forward in maintaining containerization services for the software throughout the lifetime of Solar Cruiser.
Solar Cruiser is a solar sailing mission, riding as a secondary payload to the Interstellar Mapping and Acceleration Probe (IMAP) mission, expected to launch in February of 2025. The Solar Cruiser vehicle will generate thrust via a complex, low-thrust solar sail. The extreme low-thrust nature of the solar sail will leave Solar Cruiser highly sensitive to external environmental effects (such as solar radiation pressure and high-order gravitational perturbations) throughout the entirety of flight. Because of this, preliminary & operational optimization routines must be intricately tied to high-order predictive propagation models to ensure the greatest possible confidence in mission success. The Solar Cruiser Mission Design and Navigation (MDNav) team has designed a software tool suite, employing the latest in software containerization technology, to accomplish this task, allowing for seamless development across several users and operating systems. Combining JPL’s Monte toolkit with high-performance optimizers written by researchers at the University of Alabama, the proposed architecture allows for instant verification of optimized trajectories within the same development environment that the optimization takes place, removing the need for mission designers and navigators to switch between tools. The MDNav software suite itself is separated from the development and operational scripts to be used in flight, which allows for maintaining a low-footprint version control profile – thus avoiding unnecessary file bloating. This paper discusses the historical differences between previous iterations of the Solar Cruiser MDNav tool suite and the current iteration, planned operational interfaces of the tool with other software and subsystems, and the planned path forward in maintaining containerization services for the software throughout the lifetime of Solar Cruiser.
The objective of the Mars Sample Return (MSR) campaign is to return samples from the surface of Mars to Earth for research. As one element of the MSR campaign, the Mars Ascent Vehicle (MAV) is responsible for transporting the samples from the surface of Mars to a Low-Martian Orbit (LMO) for retrieval. Complete autonomy is required throughout ascent, and orbital insertion is constrained by tight dispersion boundaries. An unguided, spin-stabilized second stage for MAV has been selected over a guided upper-stage to drive mass savings and reduce overall MSR campaign mass risk, at the cost of reduced GNC capability. To address this design change, the MAV GNC team has derived a robust prediction algorithm, building on previous energy management schemes, that solves for a single inertial pointing direction solution for the spin-stabilized 2nd stage burn. Algorithm stability is explored that compared to previous versions of the algorithm. Also, a set of analytical partials was developed to study MAV’s dispersed orbital insertion performance with respect to MAV system uncertainties. These partials were verified through simulation analysis and prove useful for analytical insight into the dynamics of MAV during the 2nd stage maneuver.
Solar Cruiser is a solar sailing mission, riding as a secondary payload to the Interstellar Mapping and Acceleration Probe (IMAP) mission, expected to launch in February of 2025. The Solar Cruiser vehicle will generate thrust via a complex, low-thrust solar sail. The extreme low-thrust nature of the solar sail will leave Solar Cruiser highly sensitive to external environmental effects (such as solar radiation pressure and high-order gravitational perturbations) throughout the entirety of flight. Because of this, preliminary & operational optimization routines must be intricately tied to high-order predictive propagation models to ensure the greatest possible confidence in mission success. The Solar Cruiser Mission Design and Navigation (MDNav) team has designed a software tool suite, employing the latest in software containerization technology, to accomplish this task, allowing for seamless development across several users and operating systems. Combining JPL’s Monte toolkit with University of Alabama’s high-performance optimizer, ASSET, the proposed architecture allows for instant verification of optimized trajectories within the same development environment that the optimization takes place, removing the need for mission designers and navigators to switch between tools. The MDNav software suite itself is separated from the development and operational scripts to be used in flight, which allows for maintaining a low-footprint version control profile – thus avoiding unnecessary file bloating. This paper discusses the historical differences between previous iterations of the Solar Cruiser MDNav tool suite and the current iteration, planned operational interfaces of the tool with other software and subsystems, and the planned path forward in maintaining containerization services for the software throughout the lifetime of Solar Cruiser.
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The Mars Ascent Vehicle (MAV) will be the first vehicle to perform an ascent from the surface of another atmospheric planetary body outside of the Earth-Moon system. Significant light-time delay requires complete autonomy of flight throughout ascent, and naturally a high level of reliability is desired in both MAV’s hardware and software subsystems. The MAV Guidance, Navigation and Controls (GNC) team and the MAV Flight Software (FSW) team have partnered together to improve the efficiency of algorithm integration onto the MAV flight processor, and to increase confidence that said integration is successful and without human error. An interface architecture is proposed for the GNC suite that allows both the guidance and navigation subsystems to provide code algorithms directly in C++, and the controls subsystem to provide MATLAB Simulink auto-coded algorithms. Several continuous integration/deployment (CI/CD) methodologies have been considered for ease of transition of algorithm code from the GNC team to the FSW team. The GNC/FSW teams also worked together to develop a cFS-friendly wrapper which abstracts the integration of the GNC algorithm code into an interface-level API that is compatible with cFS. Several iterations of vehicle GNC code have been produced between the GNC/FSW team’s partnership, and this strong interface between these two teams have allowed the GNC/FSW teams to greatly increase confidence of efficient and error-free implementation of the GNC code onto MAV for a successful flight.
Solar Cruiser is a Small Satellite Technology Demonstration Mission (TDM) to mature solar sail propulsion technology to enable near-term, high-priority breakthrough science missions as defined in the Solar and Space Physics Decadal Survey. Solar sails have the potential to provide high ΔV for many types of missions. Solar sails are large, mirror-like structures made of a lightweight material that reflects sunlight to propel the spacecraft. The continuous solar photon pressure provides thrust with no need for the heavy, expendable propellants used by conventional chemical and electric propulsion systems. Solar Cruiser will demonstrate a “sailcraft” platform with pointing control and attitude stability comparable to traditional platforms, upon which a new class of Heliophysics missions may fly. It will show sailcraft operation (acceleration, navigation, station keeping, heliocentric plane change) scalability of sail technologies such as the boom, membrane, and deployer to enable more demanding missions, such as high inclination solar imaging. Solar Cruiser will launch as a secondary payload with NASA’s Interstellar Mapping and Acceleration Probe (IMAP) in early 2025. The sailcraft will separate from the launch vehicle on a near-L1 trajectory (Sun-Earth Lagrangian Point 1; sunward of L1 along the Sun-Earth Line) and complete its primary mission in 11 months or less.
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