An enhanced anti-disturbance guidance scheme for powered descent phase of Mars landing under actuator fault

Abstract

As a class of autonomous deep-space exploration robots, Mars lander is simultaneously affected by wind disturbance and actuator fault, which hinders precise landing on Mars. In this article, we propose a composite guidance approach by combining disturbance observer and iterative learning observer together. The Mars wind disturbance is dealt by the disturbance observer providing wind estimation which is rejected through feed-forward channel. Meanwhile, the iterative learning observer is used to estimate the deficiency of actuators. The proposed guidance scheme ensures not only the precision but also the reliability of the Mars guidance system. The stability of the closed-loop system is analyzed. Simulations made in different situations demonstrate the performance of the proposed approach.

Enhancing the landing guidance of a reusable launch vehicle by improving genetic algorithm-based deep reinforcement learning using Hybrid Deterministic-Stochastic algorithm

“…Paper [ 36 ] compares RL to optimal control, but the density of sample is not determined reliably and without gradient search. [ 37 ] uses PID and observer that can be used in this research. [ 38 ] uses RL and optimal control as combined controller, is an interesting research.…”