Method And System for Enhancing Vehicle Performance and Design Using Parametric Modeling and Gradient-Based Control Integration

Vehicles designed for purposes of exploration of the planets and other atmospheric bodies in the Solar System favor the use of mid-Lift/Drag blunt body geometries. Such shapes can be designed so as to yield favorable hypersonic aerothermodynamic properties for low heating and hypersonic aerodynamic properties for maneuverability and stability. The entry trajectory selected influences entry peak heating and integrated heating loads which in turn influences the design of the thermal protection system. A nominal is used to compare each shape considered. The vehicle will be subject to both launch and entry loading along with structural integrity constraints that may further influence shape design. Further, such vehicles must be sized so as to fit on existing or realizable launch vehicles, often within existing launch payload shroud constraints.

Composite materials are being used in aerospace design because of their high strength-to-weight ratio. On modern airplanes, composite wings offer a greater degree of aerodynamic efficiency due to weight savings, but at the same time introduce more structural flexibility than their aluminum counterparts. Under off-design flight conditions, changes in the wing shape due to structural flexibility cause the wing aerodynamics to be non-optimal. This effect could offset any weight saving benefits realized by the composite wings. Structural flexibility could also cause adverse interactions with flight control and structural vibration which can compromise aircraft stability, pilot handling qualities, and passenger ride quality. NASA Ames Research Center has developed a novel technology that employs a new multi-objective flight control optimization framework to achieve multiple control objectives simultaneously. This technology leverages the availability of distributed flight control surfaces in modern transports. The multi-objective flight control technology comprises the following objectives all acting in a synergistic manner: 1) traditional stability augmentation and pilot command-following flight control, 2) drag minimization, 3) aeroelastic mode suppression, 4) gust load alleviation, and 5) maneuver load alleviation. Each of these objectives can be a major control system design in its own right. Thus, the multi-objective flight control technology can effectively manage the complex interactions of the individual single-objective flight control system design and take into account multiple competing requirements to achieve optimal flight control solutions that have the best compromise for these requirements. In addition, a real- time drag minimization control strategy is included in the guidance loop. This feature utilizes system identification methods to estimate aerodynamic parameters for the on-line optimization. The aerodynamic parameters are also used in the multi-objective flight control for drag minimization and maneuver/ gust load alleviation control.

Many control problems can benefit from the adaptive control algorithm described. This algorithm is well-suited to nonlinear applications that have unknown modeling parameters and poorly known operating conditions. Disturbance accommodation is a critical component of many systems. By using feedback control with disturbance accommodation, system performance and reliability can be increased considerably. Often the form of the disturbance is known, but the amplitude is unknown. For instance, a motor operating on a structure used for accurate pointing would cause a sinusoidal disturbance of a known frequency content. The algorithm described is able to accurately cancel these disturbances, without needing knowledge of their amplitude. In markets needing controllers, the efficiency, uptime, and lifespan of equipment can be dramatically increased due to the robustness of this technologys design.

RAM-C interfaces with computational software to provide test logic and manage a unique process that implements three main bodies of theory: (a) aircraft system identification (SID), (b) design of experiment (DOE), and (c) CFD. SID defines any number of alternative estimation methods that can be used effectively under the RAM-C process (e.g., machine learning techniques, regression, neural nets, fuzzy modeling, etc.). DOE provides a statistically rigorous, sequential approach that defines the test points required for a given model complexity. Typical DOE test points are optimized to reduce either estimation error or prediction error. CFD provides a large range of fidelity for estimating aircraft aerodynamic responses. In initial implementations, NASA researchers “wrapped” RAM-C around OVERFLOW, a NASA-developed high-fidelity CFD flow solver. Alternative computational software requiring less time and computational resources could be also utilized. RAM-C generates reduced-order aerodynamic models of aircraft. The software process begins with the user entering a desired level of fidelity and a test configuration defined in terms appropriate for the computational code in use. One can think of the computational code (e.g., high-fidelity CFD flow solver) as the “test facility” with which RAM-C communicates with to guide the modeling process. RAM-C logic determines where data needs to be collected, when the mathematical model structure needs to increase in order, and when the models satisfy the desired level of fidelity. RAM-C is an efficient, statistically rigorous, automated testing process that only collects data required to identify models that achieve user-defined levels of fidelity – streamlining the modeling process and saving computational resources and time. At NASA, the same Rapid Aero Modeling (RAM) concept has also been applied to other “test facilities” (e.g., wind tunnel test facilities in lieu of CFD software).

Significant contributors to the cost of launching nano- and micro-satellites to orbit are the costs of software, and Guidance, Navigation and Control (GNC) avionics systems that steer, navigate and control the launch vehicles, sequence stage separation, deploy payloads, and pass data to Telemetry. The high costs of these GNC avionics systems are due in part to the current practice of developing unique, custom, single-use hardware and software for each launch, and requiring high-precision measurements of position and attitude states. NASA Ames Research Center has developed and tested a low-cost avionics system prototype called Affordable Vehicle Avionics (AVA). AVA integrates a low-cost moderately-precise sensor suite with an advanced error-correcting software package to provide GNC for space launch vehicles in a package smaller than a multilayer sandwich (100 mm x 120 mm x 69 mm; 4in x 4.7in x 2.7in), and with a mass of less than 0.84kg (2lbs). The invention provides a common suite of avionics components and demonstration software that deliver affordable, capable GNC with flexible I/O which is applicable to a variety of nano/micro-sat launch vehicles at less than 10 percent of the cost to procure current state-of-the-art GNC avionics. Affordable Vehicle Avionics' (AVA's) approach to drastically reduce costs includes: (1) use of low-cost "tactical-grade" Commercial-off-the-Shelf MicroElectroMechanical Systems (MEMS) inertial measurement unit, wherein adequate navigation precision is achieved by fusing outputs from a Global Positioning System receiver, inertial sensors and a magnetic field vector sensor in an extended Kalman filter formulation that corrects inertial sensor biases; (2) a streamlined "cookbook" approach to define an effective process for launch vehicle developers to design, simulate, verify and support assembly, integration and testing of their SLVs, driven by high-fidelity six degrees of freedom SLV simulations and real-time hardware-in-loop tests to validate guidance, navigation and control for early test flights. Development Status: As of spring 2020, AVA has flown twice in its current configuration on a suborbital platform. Its navigation and control functions were successfully demonstrated for roll-rate control within a tight deadband onboard the first flight test, and it successfully issued attitude pointing commands to a failed reaction control subsystem and it issues issued a rocket-motor ignition command on a second flight test. To date, failure of SLV components other than AVA (e.g., electrical power) has precluded demonstration of navigation and control of an orbital or sub-orbital launch system, which remains to be demonstrated. AVA development was accomplished using a single magnetometer-based magnetic field vector sensor to provide attitude observability during free-fall (inter-stage coast periods). Therefore, the current tested AVA configuration is susceptible to magnetic/electric fields produced by other components and payloads onboard the SLV, so care must be exercised to either mount AVA well away from sources of such fields and or to incorporate magnetic/electric field barriers on field emitters if separation from emitters is inadequate. Also, licensees may wish to provide new AVA inputs from a pair of external horizon sensors to provide more accurate attitude navigation during coast phases of the SLV mission.