Adaptive consensus for heterogeneous unknown nonlinear multi-agent systems with asymmetric input dead-zone: A finite-time approach

Abstract

This study deals with the adaptive finite-time consensus problem of heterogeneous multi-agent systems composed of first-order and second-order agents with unknown nonlinear dynamics and asymmetric input dead-zone under connected undirected topology. Under the proposed protocol and adaptive laws, a sliding mode variable for every agent converges to a compact set in finite time, and also the position errors and the velocity errors (for second-order agents) between any two agents converge to a small desired neighborhood of the origin in finite time. Each agent requires its states and the relative positions of its neighbors. By applying sliding mode control, the external disturbances, and the imperfect approximation of neural networks are rejected. The unknown terms of the agents’ dynamics are approximated using radial basis function neural networks. The adaptive compensator plus dead-zone is applied to overcome the asymmetric input dead-zone. Based on Lyapunov stability theory, analysis is led on stability. Different from the previous works, the global information graph is not used in the proposed protocol. Finally, our approach is examined for two examples to evaluate its performance.

Practical finite-time consensus of multi-agent systems with unknown nonlinear dynamics and the asymmetric input dead zone

“…In recent years, cooperative control of multi-agent systems (MASs) has attracted intensive attention because of its widespread applications in various fields, namely multi-satellites system (Hu and Yang, 2020), fractional-order systems (Chen et al, 2021), unmanned tail-sitters (Liu et al, 2021), unmanned aerial vehicles (UAV) (Rezaei and Menhaj, 2018a), vehicular platoons (Chehardoli and Ghasemi, 2019), model predictive control (Rahimi and Binazadeh, 2020), attitude synchronization (Mihankhah and Doustmohammadi 2021), high-power systems (Lv et al 2020), and high-order systems (Lv et al, 2021). One of the underlying problems of MASs is consensus, which requires all the agents to steer to a common value under a suitable control protocol (Moradi et al, 2020; Rezaee and Abdollahi, 2015; Sinafar et al, 2020; Zamanian et al, 2021a).…”

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

“…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.…”

Event-triggered consensus for delayed second-order leader-following heterogeneous multi-agent systems under input saturated condition

“…In the past 10 years, multi-agent systems (MASs) have achieved tremendous development (Qin et al, 2017b; Liu et al, 2017; Kaviarasan et al, 2018; Zhang et al, 2017; Li et al, 2017; Dong and Hu, 2016) with the rapid advancement of computer, communication, artificial intelligence, and other technologies, and consensus is one of the important branches. Previous research on consensus assumed that each agent in first-order systems (Vicsek et al, 1995; Wang, 2010; Hao et al, 2020; Miao and Ma, 2015), second-order systems (Song et al, 2010; Zamanian et al, 2021; J.Liu et al, 2019), high-order systems (Zhou and Lin, 2014; Valcher and Misra, 2014), and discrete systems (Yang et al, 2014) has the same state. Then consensus control is also applied to vehicles (Zegers et al, 2017; Baldi et al, 2020).…”

“…In the aforementioned studies, (Qin et al, 2017b; Liu et al, 2017; Kaviarasan et al, 2018; Zhang et al, 2017; Li et al, 2017; Dong and Hu, 2016; Vicsek et al, 1995; Wang, 2010; Hao et al, 2020; Miao and Ma, 2015; Song et al, 2010; Zamanian et al, 2021; J.Liu et al, 2019; Zhou and Lin, 2014; Valcher and Misra, 2014; Yang et al, 2014; Zheng et al, 2017, 2019; Han et al, 2019; Du et al, 2020; Jiang et al, 2019; Xiao and Fan, 2021), each agent needs to communicate continuously with its neighbors. However, communication between all agents causes rapid energy consumption, especially under the premise of limited communication network resources in reality.…”