论文标题
通过功能连接理论对大行星体的省油下降指导
Fuel-Efficient Powered Descent Guidance on Large Planetary Bodies via Theory of Functional Connections
论文作者
论文摘要
在本文中,我们提出了一种新的方法,可以使用最近开发的功能连接理论来解决没有大气(例如月球或火星)的大行星体(例如月球或火星)上的燃油效率下降引导问题。该问题是使用间接方法提出的,该方法将最佳的指导问题施加为非线性两点边界值问题的系统。使用功能连接理论,将问题约束分析嵌入到“约束表达式”中,该表达式保持了自由功能,该自由功能使用具有未知系数的正交多项式扩展。不管未知系数的值如何,将两点边界值问题转换为不受约束的优化问题,这些约束都得到满足。该过程将解决方案投入了问题的可允许子空间中,因此可以使用简单的数值技术(即,在本文中,使用了非线性最小二乘方法)。除了推导该技术外,该方法在两种情况下进行了验证,结果将与通用最佳控制软件GPOPS-II获得的结果进行比较。通常,提出的技术生产$ \ Mathcal {O}(10^{ - 10})$的解决方案。此外,对于拟议的测试用例,据报道,每个基于TFC的内环迭代都在6个迭代范围内收敛,每次迭代在MATLAB传统实现中显示出72至81毫秒之间的计算时间。因此,所提出的方法可能适用于实时生成最佳轨迹的板载。
In this paper we present a new approach to solve the fuel-efficient powered descent guidance problem on large planetary bodies with no atmosphere (e.g. the Moon or Mars) using the recently developed Theory of Functional Connections. The problem is formulated using the indirect method which casts the optimal guidance problem as a system of nonlinear two-point boundary value problems. Using the Theory of Functional Connections, the problem constraints are analytically embedded into a "constrained expression," which maintains a free-function that is expanded using orthogonal polynomials with unknown coefficients. The constraints are satisfied regardless of the values of the unknown coefficients which convert the two-point boundary value problem into an unconstrained optimization problem. This process casts the solution into the admissible subspace of the problem and therefore simple numerical techniques can be used (i.e. in this paper a nonlinear least-squares method is used). In addition to the derivation of this technique, the method is validated in two scenarios and the results are compared to those obtained by the general purpose optimal control software, GPOPS-II. In general, the proposed technique produces solutions of $\mathcal{O}(10^{-10})$. Additionally, for the proposed test cases, it is reported that each individual TFC-based inner-loop iteration converges within 6 iterations, each iteration exhibiting a computational time between 72 and 81 milliseconds within the MATLAB legacy implementation. Consequently, the proposed methodology is potentially suitable for on-board generation of optimal trajectories in real-time.