Robust Formation Control for Multiagent Systems Based on Adaptive Observers

Fuzzy adaptive observer–based resilient formation control for heterogeneous multiple unmanned aerial vehicles with false data injection attacks and prescribed performance

“…Note that Assumption 1 is a common condition on the connectivity of the network. In the previous formation control schemes, such as those Yan et al (2022) and Chen et al (2022), u 0 is assumed that u 0 is known or u 0 = 0 . Therefore, Assumption 2 is a more general assumption.…”

“…However, these heterogeneous formation control strategies assumed that the exact system models should be known, which is quite restrictive in practical application due to the complex dynamics of UAVs. Recently, the distributed consistency estimator-based framework in Zou and Meng (2019), Yan et al (2022), and Chen et al (2022) has risen much attention, which can transform the heterogeneous formation and consensus control problem into a tracking control problem of each agent. Although the distributed consistency estimator can simplify the heterogeneous formation control problem, these existing estimators are established on the assumption that the leader's input is available or is zero.…”

“…For example, the formation systems usually require reconnaissance and attack UAVs to cooperate in search-attack missions (Zhen et al, 2019). Some remarkable attempts are committed to solving cooperative control for heterogeneous multi-UAV systems or MAS, which involve but are not limited to finite-time approach (Zamanian et al, 2022), adaptive radial basis function neural network (Ren et al, 2021), reinforcement learning strategy (Liu et al, 2020), optimal control (Wang et al, 2021), event-triggered control (Gao et al, 2021), and distributed consistency estimator–based control scheme (Chen et al, 2022; Yan et al, 2022; Zou and Meng, 2019). However, these heterogeneous formation control strategies assumed that the exact system models should be known, which is quite restrictive in practical application due to the complex dynamics of UAVs.…”

“…Different from the formation control approaches in Shao et al (2022), Han et al (2021), Rosa et al (2019), Dehghani and Menhaj (2016), Zhang et al (2019), Zamanian et al (2022), Ren et al (2021), Liu et al (2020), Wang et al (2021), Gao et al (2021), Zou and Meng (2019), Yan et al (2022), and Chen et al (2022), the transient formation error constraint of each UAV can be guaranteed by a novel PPC method, which satisfies the demands in safe operation. Meanwhile, the requirement for initial states in the traditional PPC method (Ma et al, 2021; Tong et al, 2015; Zhang et al, 2016; Zhao et al, 2018; Zhou et al, 2020; Zhu et al, 2022) can be completely relaxed.…”

Finite-time ADRC formation control for uncertain nonaffine nonlinear multi-agent systems with prescribed performance and input saturation

“…Formation control of MASs has been studied extensively over the past decades (see, refs. [8][9][10][11][12][13][14][15][16][17][18] and the references therein). Neural network-based, robust control, and event-triggered method are used respectively to address formation control problems for MASs with unknown nonlinear dynamics [8,9,17,18], with system uncertainties and external disturbances [13,14] and with switching topologies [15,16].…”