Adaptive flocking of non‐linear multi‐agents systems with uncertain parameters

Zhang, Qing; Li, Ping; Yang, Zhengquan; Chen, Zengqiang

doi:10.1049/iet-cta.2014.0471

Cited by 26 publications

(19 citation statements)

References 21 publications

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“…Several research groups have been contributed for improving the flocking behavior of networked multi-UAS in recent years [9][10][11][12]. Groups of aircrafts, in many applications involving networked multi-UAS, require communicating in order to successfully accomplish their assigned tasks.…”

Section: Motivationmentioning

confidence: 99%

“…Appendix A.1. Non-Adapting Phase Our goal is to investigate the output of the system in non-adapting phase (i.e., when the system completes its learning process) so the Equations (10) and (11) which are the updating rules of Amygdala and Orbitofrontal Cortex, respectively, should be taken into consideration. In addition, we make an assumption that the max function in Equation (10) could be neglected.…”

Section: Conflicts Of Interestmentioning

confidence: 99%

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Biologically-Inspired Intelligent Flocking Control for Networked Multi-UAS with Uncertain Network Imperfections

Jafari

2018

Drones

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In this paper, a biologically-inspired distributed intelligent control methodology is proposed to overcome the challenges, i.e., networked imperfections and uncertainty from the environment and system, in networked multi-Unmanned Aircraft Systems (UAS) flocking. The proposed method is adopted based on the emotional learning phenomenon in the mammalian limbic system, considering the limited computational ability in the practical onboard controller. The learning capability and low computational complexity of the proposed technique make it a propitious tool for implementing in real-time networked multi-UAS flocking considering the network imperfection and uncertainty from environment and system. Computer-aid numerical results of the implementation of the proposed methodology demonstrate the effectiveness of this algorithm for distributed intelligent flocking control of networked multi-UAS.

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Section: Motivationmentioning

confidence: 99%

Section: Conflicts Of Interestmentioning

confidence: 99%

Biologically-Inspired Intelligent Flocking Control for Networked Multi-UAS with Uncertain Network Imperfections

Jafari

2018

Drones

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“…Through the comparison made between this paper and [18][19][20], the following advantages can be obtained.…”

Section: Introductionmentioning

confidence: 99%

“…In contrast, high-frequency feedback robust control for flocking of multi-agent with unknown parameters can not only be used to identify unknown parameters, but also has perfect robustness. In comparison with the flocking of a multi-agent system in [18][19][20], it can be observed that high-frequency feedback robust control for flocking of multi-agent with unknown parameters has better performance in both convergence and stability.…”

Section: Introductionmentioning

confidence: 99%

High-frequency feedback robust control for flocking of multi-agent system with unknown parameters

et al. 2019

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In this paper, a kind of high-frequency feedback robust control for flocking of a multi-agent system with unknown parameters is put forward in detail. Firstly, a high-frequency feedback robust control scheme is proposed for the flocking problem of a multi-agent system with unknown parameters. Secondly, through the employment of Lyapunov stability theory, it's proved that velocity error is gradually stabilized and collisions between agents are also avoided. In addition, the high-frequency feedback robust control for flocking of a multi-agent system with unknown parameters is further proved. Finally, it is verified with the help of numerical simulation.

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“…Inspired by the potential applications in engineering, such as networked autonomous vehicles, sensor networks [1], and formation control [2], distributed coordination of multiagent systems has attracted much attention from researchers [3,4]. Very recently, consensus problems have been studied extensively as well as references therein [5,6].…”

Section: Introductionmentioning

confidence: 99%

Distributed Tracking Control for Discrete-Time Multiagent Systems with Novel Markovian Switching Topologies

Wang

Zhou

2017

Discrete Dynamics in Nature and Society

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Distributed discrete-time coordinated tracking control problem is investigated for multiagent systems in the ideal case, where agents with a fixed graph combine with a leader-following group, aiming to expand the function of the traditional one in some scenes. The modified union switching topology is derived from a set of Markov chains to the edges by introducing a novel mapping. The issue on how to guarantee all the agents tracking the leader is solved through a PD-like consensus algorithm. The available sampling period and the feasible control gain are calculated in terms of the trigonometric function theory, and the mean-square bound of tracking errors is provided finally. Simulation example is presented to demonstrate the validity of the theoretical results.

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Adaptive flocking of non‐linear multi‐agents systems with uncertain parameters

Cited by 26 publications

References 21 publications

Biologically-Inspired Intelligent Flocking Control for Networked Multi-UAS with Uncertain Network Imperfections

Biologically-Inspired Intelligent Flocking Control for Networked Multi-UAS with Uncertain Network Imperfections

High-frequency feedback robust control for flocking of multi-agent system with unknown parameters

Distributed Tracking Control for Discrete-Time Multiagent Systems with Novel Markovian Switching Topologies

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