Adaptive Multivariable Reentry Attitude Control of RLV With Prescribed Performance

Tian, Bailing; Li, Zhiyu; Zhao, Xiaopeng; Zong, Qun

doi:10.1109/tsmc.2022.3145508

Cited by 12 publications

(1 citation statement)

References 26 publications

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“…In this context, attitude control for RLV is a challenging topic and has elicited widespread interest. Various control methodologies, such as adaptive control [2], dynamic inversion control [3], robust control [4], sliding mode control [5,6], and neural network (NN) control [7,8], have been applied over the past decades. Nevertheless, there is still scope to develop an optimal control approach for RLV suffering from complicated non-linear dynamics, parametric uncertainties and limited inputs.…”

Section: Introductionmentioning

confidence: 99%

Reinforcement-Learning-Based Tracking Control with Fixed-Time Prescribed Performance for Reusable Launch Vehicle under Input Constraints

Guan

Wei

et al. 2022

Applied Sciences

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This paper proposes a novel reinforcement learning (RL)-based tracking control scheme with fixed-time prescribed performance for a reusable launch vehicle subject to parametric uncertainties, external disturbances, and input constraints. First, a fixed-time prescribed performance function is employed to restrain attitude tracking errors, and an equivalent unconstrained system is derived via an error transformation technique. Then, a hyperbolic tangent function is incorporated into the optimal performance index of the unconstrained system to tackle the input constraints. Subsequently, an actor-critic RL framework with super-twisting-like sliding mode control is constructed to establish a practical solution for the optimal control problem. Benefiting from the proposed scheme, the robustness of the RL-based controller against unknown dynamics is enhanced, and the control performance can be qualitatively prearranged by users. Theoretical analysis shows that the attitude tracking errors converge to a preset region within a preassigned fixed time, and the weight estimation errors of the actor-critic networks are uniformly ultimately bounded. Finally, comparative numerical simulation results are provided to illustrate the effectiveness and improved performance of the proposed control scheme.

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