Neural network-based distributed adaptive configuration containment control for satellite formations

Abstract

Considering unknown perturbations and model uncertainties, this paper investigates the configuration containment control for satellite formations based on directed communication topologies. We consider that there are multiple leader satellites with constant relative velocities, while only a subset of follower satellites have access to the leaders. First, a distributed velocity observer is proposed for each follower to obtain velocity information of the leaders. Then, a distributed adaptive configuration containment control algorithm is proposed in which the model nonlinear uncertainties are approximated and compensated through neural networks, while the perturbations and approximation errors are compensated by adaptive gain technique. Furthermore, subject to the chattering caused by sign functions, an improved continuous containment strategy is developed. We use graph theory, Lyapunov theory, and Barbalat lemma to demonstrate that both proposed methods can make all the follower satellites converge to the convex hull spanned by the leader satellites. Numerical examples and comparisons are provided to show the effectiveness and performances of the proposed control strategies.

Orbital Containment Control Algorithm and Complex Information Topology Design for Large-Scale Cluster of Spacecraft

“…The objective of containment control is to guarantee that all the followers will asymptotically converge into the convex hull formed by the leaders through information interaction and coordinated control. The containment control problem received significant research interest due to its various applications, such as mobile robots [ 13 ] , unmanned aerial vehicles(UAVs) [ 14 ] , underwater vehicles [ 15 ] and satellite formation systems [16,17] , etc.…”

“…In view of information topology, fixed network topology [16,20,23,24] and switching network topology [19] , undirected graph [21] and directed graph [18,24] are considered. In many researches, containment control is combined with other novel control algorithms, such as finite-time control [15,16,23] , adaptive control [15,17] , neural network [17] , event-triggered control [ 27 ] , etc. In addition, other problems such as time delay [12,22] , model uncertainties [16,17,24] , external disturbances [16,17] are also discussed.…”

“…In many researches, containment control is combined with other novel control algorithms, such as finite-time control [15,16,23] , adaptive control [15,17] , neural network [17] , event-triggered control [ 27 ] , etc. In addition, other problems such as time delay [12,22] , model uncertainties [16,17,24] , external disturbances [16,17] are also discussed.…”

“…Under directed topology, [16] investigated distributed finite-time configuration containment control problem for satellite formation with model uncertainties and perturbations. Moreover, a continuous containment control protocol was proposed for formation satellites modeled by EL equations in [17]. Based on the approximation ability of neural network(NN) and adaptive gain techniques, the model uncertainties and disturbances were compensated for.…”

Distributed finite‐time containment control for multiple Euler‐Lagrange systems with communication delays

“…In these cases, all the followers should move into the convex hull formed by the leaders, which can be named as containment control. Containment control can be used in satellite formation systems, autonomous underwater vehicles, and robot swarms . On the other hand, the research works of Ren, Ye et al, Zhang et al, Bouteraa et al, Yang et al, Yang et al, and Zhao et al did not consider the model uncertainties and external disturbances, which cannot be neglected for most practical systems.…”