CACLA-Based Trajectory Tracking Guidance for RLV in Terminal Area Energy Management Phase

Abstract

This paper focuses on the trajectory tracking guidance problem for the Terminal Area Energy Management (TAEM) phase of the Reusable Launch Vehicle (RLV). Considering the continuous state and action space of this guidance problem, the Continuous Actor–Critic Learning Automata (CACLA) is applied to construct the guidance strategy of RLV. Two three-layer neuron networks are used to model the critic and actor of CACLA, respectively. The weight vectors of the critic are updated by the model-free Temporal Difference (TD) learning algorithm, which is improved by eligibility trace and momentum factor. The weight vectors of the actor are updated based on the sign of TD error, and a Gauss exploration is carried out in the actor. Finally, a Monte Carlo simulation and a comparison simulation are performed to show the effectiveness of the CACLA-based guidance strategy.

Adpative multivarite RLV reentry attitude control with pre-specified performance in the presence of unmatched disturbances

“…Reusable launch vehicles (RLVs) are increasingly becoming popular research objects in the control community due to their tremendous value in military and civil aspects brought by their high flight speed and wide flight range [1][2][3][4][5]. However, RLVs have distinct characteristics, such as no power in the reentry stage, a small lift-drag ratio, and apparent changes in aerodynamic parameters.…”