Adaptive and Efficient Model Predictive Control for Booster Reentry

TL;DR

This paper presents an adaptive and efficient model predictive control approach tailored for booster reentry, aiming to enhance control performance while managing constraints during atmospheric descent.

Contribution

The paper introduces a novel adaptive MPC method specifically designed for booster reentry, improving computational efficiency and control accuracy over existing approaches.

Findings

Enhanced control accuracy during reentry

Reduced computational load of MPC algorithms

Improved handling of reentry constraints

Abstract

Model predictive control (MPC) is an optimal control strategy where control input calculation is based on minimizing the predicted tracking error over a finite horizon that moves with time. This strategy has an advantage over conventional state feedback and output feedback controllers because it predicts the response of the system, rather than simply reacting to it. Therefore, MPC can offer improved performance in the presence of input and output constraints. Many implementations of MPC on aerospace vehicles appear in literature [1]. Some of these include spacecraft and satellite attitude control [2-4], spacecraft rendezvous and docking [5], helicopters [6], and atmospheric re-entry [7, 8].