Solving Robust Optimal Control Problems, with Application to Spacecraft Entry, Descent and Landing

解决鲁棒最优控制问题，并将其应用于航天器进入、下降和着陆

• 批准号: 2663401
• 金额: --
• 依托单位: Imperial College London
• 依托单位国家: 英国
• 项目类别: Studentship
• 财政年份: 2020
• 资助国家: 英国
• 起止时间: 2020 至 无数据
• 项目状态: 未结题
• 来源: https://gtr.ukri.org/projects?ref=studentship-2663401
• 关键词: Solving; Robust; Optimal; Control; Problems

As space agencies and enterprises plan for future Mars missions with sequential spacecraft landings and large payload requirements, novel entry vehicles are currently being conceptualised. These spacecraft require control algorithms capable of achieving unprecedented landing precision, optimally managing thermal loads and propellent usage, while being robust to operate in highly uncertain atmospheric environments. With this application in mind, the research project aims to solve the Robust Optimal Control Problem (OCP) by faithfully including the uncertainties and nonlinearities of the model, achieving higher performance than current algorithms that use linear, time-varying basis functions for the feedback policy. The research will focus on the use of more general, neural networks as basis functions.Firstly, in the interest of solving optimal control problems - which can be posed as solving a system of differential algebraic equations (DAE) - an initial focus will be given to weighted residual methods, continuing previous work within the research group. Research will be carried in maturing this algorithm to be efficiently used for solving DAEs arising from optimal control problems. Despite the methods being general - able to solve a wide variety of OCPs (and DAEs) - special attention will be given to the spacecraft entry problem, targeting research towards the uncertain and nonlinear nature of the problem.Secondly, catering for the intrinsic uncertainty of the atmospheric environment, sensitive initial conditions, and vehicle design parameters of the problem in study, research will be carried in modelling these uncertainties. With an initial focus on polynomial chaos expansions and sample-based uncertainty as tools to explicitly represent, propagate and operate on the uncertainty of variables, which have been demonstrated to have better performance than traditional Monte Carlo methods. This is the backbone of the robustness in control algorithms this project aims to develop.Thirdly, given the significant nonlinear and non-smooth aspects of the spacecraft entry problem (e.g. three-dimensional attitude dynamics, supersonic and hypersonic effects, rocket staging and deployable structures, etc.) the use of nonlinear and non-smooth basis functions will be assessed. With a focus on neural networks, research will be carried on how they can be best used within a robust optimal control problem. An emphasis will be placed in including vehicle parameters (e.g. heat shield thickness, centre-of-mass position, actuator limits, etc.) in the dynamic optimisation process, yielding optimal spacecraft designs for a certain mission specification.This research project aims to output significant breakthroughs and improvements to current methods, theory and technology. Additionally, open-source software to solve differential equations, optimal control problems, trajectory optimisation problems, and overall spacecraft entry design optimisation will be developed and made available to industry and the research community.

由于太空机构和企业计划未来的火星任务，具有连续的航天器降落和庞大的有效载荷要求，目前正在概念化新颖的入门车辆。这些航天器需要控制算法，能够达到前所未有的着陆精度，最佳地管理热负载和推动使用，同时在高度不确定的大气环境中运行。考虑到此应用，该研究项目旨在通过忠实地包括模型的不确定性和非线性来解决可靠的最佳控制问题（OCP），比使用线性，时间变化的基础函数的当前算法更高的性能。这项研究将集中于使用更通用的神经网络作为基础函数。首先是为了解决最佳控制问题 - 可以提出作为解决差异代数方程式（DAE）系统 - 最初将重点放在加权的残留方法上，继续研究小组中的先前工作。将进行研究，以使该算法有效地用于解决由最佳控制问题引起的DAE。尽管方法是一般的 - 能够解决各种OCP（和DAE），但将特别关注航天器的进入问题，将研究的针对问题的不确定性和非线性性质进行针对问题。首先，迎合了大气环境的内在不确定性，敏感的初始条件和车辆的设计参数，将在研究中进行研究，并在研究中进行研究。最初的重点是多项式混乱的扩展和基于样本的不确定性，作为显式表示，传播和运作变量的不确定性的工具，这些变量已被证明比传统的蒙特卡洛方法具有更好的性能。这是控制算法中鲁棒性的骨干，该项目旨在发展。鉴于航天器进入问题的重要非线性和非平滑方面（例如，三维态度动力学，超音速和高超音速效应，火箭分阶段和可部署的结构等）的使用是非线性基础和非斜式基础功能。以神经网络为重点，将进行研究如何在强大的最佳控制问题中最好地使用它们。在动态优化过程中，将重点放在包括车辆参数（例如，隔热板厚度，质量中心位置，执行器限制等）中，为特定任务规范提供了最佳的航天器设计。该研究项目旨在输出对当前方法，理论和技术的显着突破性的突破和改进。此外，将开发并为行业和研究社区提供开发方程，最佳控制问题，轨迹优化问题以及整体航天器进入设计优化的开源软件。