On-line guidance with trajectory constraints for terminal area energy management of re-entry vehicles

Abstract

In this article, an on-line guidance strategy for terminal area energy management phase of a re-entry flight is proposed. In order to fulfil the objectives of this flight phase, the algorithm continuously performs long- and short-term on-line trajectory generation, also accounting for the most relevant vehicle and trajectory constraints. Long-term guidance computes a reference trajectory which minimizes the distance from the final target. Short-term guidance generates the control commands by computing a trajectory that minimizes the displacement from the reference one, thus compensating for the errors due to the environmental disturbances and to the uncertainties of the vehicle model. In the proposed guidance strategy, the main vehicle performance constraints are appropriately accounted for, thus guaranteeing adaptivity in the failure situations where the manoeuvring capabilities are reduced. The proposed guidance strategy has been developed in the framework of the unmanned space vehicle program of the Italian Aerospace Research Centre for the execution of Dropped Transonic Flight Test second mission. In this article, the algorithm performances and robustness to the vehicle initial state have been assessed through a preliminary Monte Carlo analysis. Furthermore, several simulations with bank angle and angle of attack limitations have shown the effectiveness of the algorithm in the presence of reduced manoeuvring capabilities.

Onboard guidance system design for reusable launch vehicles in the terminal area energy management phase

“…Thus, the constant dynamic pressure profile is developed as the reference one in the middle phase. Moreover, in comparison of the Mach number profile developed in [16] and dynamic pressure profile used in [27], the longitudinal profile proposed above shows a more explicit physical meaning.…”

“…Afterwards this concept is integrated into a planning and estimation algorithm to calculate the best HAC position, as well as its deviation in response to abnormal conditions [15]. In [16], a longterm and short-term online trajectory generation scheme is proposed, accounting for the most relevant vehicle and trajectory constraints. In [17], an online trajectory planning and guidance approach is proposed, where the reference path can be adjusted online.…”

“…The reference longitudinal profile in these methods is usually defined by an altitude profile as a quadratic polynomial of ground-track range or a Mach profile as a cubic polynomial of altitude. However, the physical interpretation of such a longitudinal profile requires more explanations, taking [16] as example. On the other hand, it might be impossible to use a single fixed longitudinal profile in the event of off-nominal conditions, which is also concluded in [13] and [17].…”

Gain Tuning of Flight Control Laws for Satisfying Trajectory Tracking Requirements

“…Note that the FCL analyzed in this section is significantly different from the ones implemented for the DTFT2 mission (see, for instance, Morani et al, 2011, for a detailed description of the guidance law). Given the nonlinear augmented longitudinal dynamics of the FTB1 vehicle in the DTFT2 mission, the aim of the present analysis is to find (a set of) the controller's gains compliant to a requirement expressed as inclusion in a solution's tube.…”

Neural Network Assisted Inverse Dynamic Guidance for Terminally Constrained Entry Flight

“…Entry guidance law was first successfully applied in the Apollo program [ 1 , 2 ], which was a reference tracking method. Thenceforth, an array of guidance techniques based on reference tracking has followed [ 1 , 3 – 10 ]. Essentially, these methods are applications of different optimal control laws, such as linear quadratic regulator (LQR) [ 4 , 5 ], state dependent Riccati equation (SDRE) [ 6 ], feedback linearization [ 7 , 11 ], and pseudospectral control laws [ 9 , 10 ].…”