Multi-objective optimal formation reconfiguration with scalarization and stochastic boundaries

Hou, Liqiang; Wu, Shufan

doi:10.1016/j.asr.2020.08.032

Cited by 3 publications

(2 citation statements)

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“…Based on the sliding mode surface (15) and NN, an LNN is designed to compensate for the synthesized perturbation Γ i (t). In the LNN, a P-type iterative learning algorithm is adopted to update the weights of the NN.…”

Section: Lntsmc-based Spacecraft Formation Reconfigurationmentioning

confidence: 99%

“…High-precision SFF control is another key issue in spacecraft formation reconfiguration. Traditional spacecraft formation reconfiguration methods, such as optimal control [15] [16], sliding mode control (SMC) [17], and pulse control [18], can only ensure global or local convergence of SFF systems, but the steady-state and transient performance of SFF systems cannot be predetermined [19]. The prescribed performance control (PPC) [20] can pre-specify the system tracking performance, such as maximum overshoot, tolerable range of tracking accuracy, etc.…”

Section: Introductionmentioning

confidence: 99%

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Neural Network-based Prescribed Performance Fault-Tolerant Control for Spacecraft Formation Reconfiguration with Collision Avoidance

Jia,

Shu,

et al. 2023

Preprint

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This article investigates the issue of neural network (NN)-based prescribed performance control with collision avoidance ability for a spacecraft formation system in presence of space perturbations and thruster faults. First, an artificial potential function is constructed to guarantee that spacecraft remain within communication range and collision-free. Then, a prescribed performance function is introduced such that the position errors are constrained within a preset boundary. Further, a learning non-singular terminal sliding mode control (LNTSMC) law is explored to ensure that the steady-state and transient performance of the position tracking errors satisfy the prescribed performance constraints. A novel learning NN model is utilized to estimate and compensate for the synthesized perturbation, in which an iterative learning algorithm is proposed to update the weights of the NN, reducing the computational burden. The proposed LNTSMC scheme can effectively solve issues of the inter-spacecraft collision avoidance, prescribed performance, and robust fault tolerance. Numerical simulations and comparisons are provided to demonstrate the effectiveness and superiority of the presented approach.

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Section: Lntsmc-based Spacecraft Formation Reconfigurationmentioning

confidence: 99%