Optimal reusable rocket landing guidance: a cutting-edge approach integrating scientific machine learning and enhanced neural networks

This study presents an innovative approach that utilizes scientific machine learning and two types of enhanced neural networks for modeling a parametric guidance algorithm within the framework of ordinary differential equations to optimize the landing phase of reusable rockets. Our approach addresses various challenges, such as reducing prediction uncertainty, minimizing the need for extensive training data, improving convergence speed, decreasing computational complexity, and enhancing prediction accuracy for unseen data. We developed two distinct enhanced neural network architectures to achieve these objectives: Adaptive (AQResNet) and Rowdy Adaptive (RAQResNet) Quadratic Residual Neural Networks. These architectures exhibited outstanding performance in our simulations. Notably, the RAQResNet model achieved a validation loss approximately 300 times lower than the standard architecture with an equal number of trainable parameters and 50 times lower than the standard architecture with twice the number of trainable parameters. Furthermore, these models require significantly less computational power, enabling real-time computation on modern flight hardware. The inference times of our proposed models were measured in approximately microseconds on a single-board computer. Additionally, we conducted an extensive Monte Carlo analysis that considers a wide range of factors, extending beyond aerodynamic uncertainty, to assess the robustness of our models. The results demonstrate the impressive adaptability of our proposed guidance policy to new conditions and distributions outside the training domain. Overall, this study makes a substantial contribution to the field of reusable rocket landing guidance and establishes a foundation for future advancements

Convex optimization of launch vehicle ascent trajectories

This thesis investigates the use of convex optimization techniques for the ascent trajectory design and guidance of a launch vehicle. An optimized mission design and the implementation of a minimum-propellant guidance scheme are key to increasing the rocket carrying capacity and cutting the costs of access to space. However, the complexity of the launch vehicle optimal control problem (OCP), due to the high sensitivity to the optimization parameters and the numerous nonlinear constraints, make the application of traditional optimization methods somewhat unappealing, as either significant computational costs or accurate initialization points are required. Instead, recent convex optimization algorithms theoretically guarantee convergence in polynomial time regardless of the initial point. The main challenge consists in converting the nonconvex ascent problem into an equivalent convex OCP. To this end, lossless and successive convexification methods are employed on the launch vehicle problem to set up a sequential convex optimization algorithm that converges to the solution of the original problem in a short time. Motivated by the computational efficiency and reliability of the devised optimization strategy, the thesis also investigates the suitability of the convex optimization approach for the computational guidance of a launch vehicle upper stage in a model predictive control (MPC) framework. Being MPC based on recursively solving onboard an OCP to determine the optimal control actions, the resulting guidance scheme is not only performance-oriented but intrinsically robust to model uncertainties and random disturbances thanks to the closed-loop architecture. The characteristics of real-world launch vehicles are taken into account by considering rocket configurations inspired to SpaceX's Falcon 9 and ESA's VEGA as case studies. Extensive numerical results prove the convergence properties and the efficiency of the approach, posing convex optimization as a promising tool for launch vehicle ascent trajectory design and guidance algorithms

Green Propulsion Demonstrator “The LÄNDer”

For future manned and unmanned space missions, landing systems are required, which are able to initiate and carry out soft autonomous landings on extraterrestrial celestial bodies. Development of rapid and robust guidance and control (GNC) systems as well as efficient, controllable and safe thrusters meeting a high level of autonomy and safety is crucial for successful lander missions. To meet those requirements, both component level view such as physico-chemical behavior of the propellants and the transients of the propulsion system itself as well as the system level view of the behavior of the holistic system are of utmost interest. To enable extensive testing and demonstration of new GNC methods and rocket engines, DLR Lampoldshausen has extended its research fields to develop a modular lander platform and a test bench with variable degrees of freedom. The aim is to combine extensive testing of GNC algorithms with sustainable propellant technology

Abstracts to Be Presented at the 2015 Supercomputing Conference

Compilation of Abstracts to be presented at the 2015 Supercomputing Conferenc

Proceedings of the Seventh Annual Summer Conference. NASA/USRA: University Advanced Design Program

The Advanced Design Program (ADP) is a unique program that brings together students and faculty from U.S. engineering schools with engineers from the NASA centers through integration of current and future NASA space and aeronautics projects into university engineering design curriculum. The Advanced Space Design Program study topics cover a broad range of projects that could be undertaken during a 20-30 year period beginning with the deployment of the Space Station Freedom. The Advanced Aeronautics Design Program study topics typically focus on nearer-term projects of interest to NASA, covering from small, slow-speed vehicles through large, supersonic passenger transports and on through hypersonic research vehicles. Student work accomplished during the 1990-91 academic year and reported at the 7th Annual Summer Conference is presented

Engineering Advantage, Spring 2011

https://digitalcommons.calpoly.edu/ceng_news/1001/thumbnail.jp

Engineering Advantage, Spring 2011

https://digitalcommons.calpoly.edu/ceng_news/1001/thumbnail.jp