Distributed Adaptive Bipartite Time-Varying Formation Control for Heterogeneous Unknown Nonlinear Multi-Agent Systems

Wang, Weike; Zhang, Wei; Yan, Chenhang; Fang, Yu

doi:10.1109/access.2021.3068966

Cited by 7 publications

(2 citation statements)

References 30 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…For linear heterogeneous multi‐agents, a distributed PI and PID control scheme is proposed 19,20 . The control method 21 for unknown nonlinear heterogeneous multi‐agents estimates the unfamiliar nonlinear terms by adaptive control.…”

Section: Introductionmentioning

confidence: 99%

Heterogeneous swarm control based on two‐layer topology

Chen

Fan

Yang

et al. 2023

Intl J Robust & Nonlinear

View full text Add to dashboard Cite

Summary In this paper, we propose a heterogeneous swarm control method based on a two‐layer communication topology, which solves the problems of great difficulty in controller design and high computational complexity of communication topology in large‐scale heterogeneous swarm control. Firstly, we divide large‐scale heterogeneous swarms into several independent isomorphic sub‐swarm and design corresponding sub‐swarm controllers, which dramatically reduces the difficulty of swarm controller design. Secondly, we introduce a two‐layer communication topology to treat the followers in the first‐layer communication topology as leaders in the second‐layer communication topology, which establishes the connection between each sub‐swarm and the global leader and reduces the computational complexity of the communication topology. Finally, based on the two‐layer communication topology, the swarm controllers in the first‐layer and second‐layer communication topologies are designed using the sliding mode and dynamic surface methods. The Lyapunov function is designed to prove its stability. The simulation results verify the effectiveness of the controllers.

show abstract

Section: Introductionmentioning

confidence: 99%

Heterogeneous swarm control based on two‐layer topology

Chen

Fan

Yang

et al. 2023

Intl J Robust & Nonlinear

View full text Add to dashboard Cite

show abstract

“…However, it has a problem that has been diminished owing to ongoing hardware improvements. The proposed method has been used to develop time-varying [12,20] and real-time applications [21] such as mobile robotics. The RL-based multi-agent approach can counter many different problems, such as machine learning, to solve multi-agent coordination and collaboration [22,23].…”

Section: Introductionmentioning

confidence: 99%

Autonomous and Asynchronous Triggered Agent Exploratory Path-planning Via a Terrain Clutter-index using Reinforcement Learning

Kim

2022

JICCE

View full text Add to dashboard Cite

An intelligent distributed multi-agent system (IDMS) using reinforcement learning (RL) is a challenging and intricate problem in which single or multiple agent(s) aim to achieve their specific goals (sub-goal and final goal), where they move their states in a complex and cluttered environment. The environment provided by the IDMS provides a cumulative optimal reward for each action based on the policy of the learning process. Most actions involve interacting with a given IDMS environment; therefore, it can provide the following elements: a starting agent state, multiple obstacles, agent goals, and a cluttered index. The reward in the environment is also reflected by RL-based agents, in which agents can move randomly or intelligently to reach their respective goals, to improve the agent learning performance. We extend different cases of intelligent multi-agent systems from our previous works: (a) a proposed environment-clutter-based-index for agent sub-goal selection and analysis of its effect, and (b) a newly proposed RL reward scheme based on the environmental clutter-index to identify and analyze the prerequisites and conditions for improving the overall system.

show abstract