Cyclic Pursuit-Fuzzy PD Control Method for Multi-agent Formation Control in 3D Space

Hui-xin, Yang; Wang, Yueying

doi:10.1007/s40815-020-00892-z

Cited by 9 publications

(7 citation statements)

References 13 publications

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“…His idea is to use nonlinear loop tracking control in the longitudinal movement of the formation and use a piecewise proportional derivative (PD) controller based on fuzzy control for the cooperative control of the normal movement. e results show that this method improves the performance and accuracy of formation reconfiguration control, avoids collisions between members, and enhances the stability of the system [1]. e methodology used by M'Hammedi is an evaluation of the main methods used in the creation of the bare new city of Rabat.…”

Section: Introductionmentioning

confidence: 99%

Construction and Analysis of Urban Cultural Plane Space Mode considering Particle Swarm Cultural Scientific Computing Algorithm

2022

Security and Communication Networks

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In the tide of economic and social internationalization and high-speed urbanization, urban culture has increasingly attracted widespread attention from all walks of life and has a very critical strategic significance in the construction of urban space. This paper aims to construct and analyze the plane space mode of urban culture based on the particle swarm cultural scientific computing algorithm. This article first explains the concept of urban cultural space. Urban cultural space uses urban spatial structure as a media carrier and uses urban cultural time for vertical extension and development; then we proposed the particle swarm cultural scientific calculation algorithm and gave the particle swarm algorithm flowchart; then based on the particle swarm optimization algorithm, the evaluation of the configuration performance of urban cultural facilities is researched and discussed, and at the same time, the evolution law of urban spatial morphology is explored based on the particle swarm optimization algorithm. The particle swarm algorithm is a random search algorithm based on group cooperation developed by simulating the foraging behavior of birds. Urban cultural space is the development of urban space based on urban culture. It plays a special and important role in urban cultural development and urban space planning. According to the statistics of the survey and research results, the utilization rate of city-level cultural facilities in City A is 77, the utilization rate of district-level cultural facilities is 72, the utilization rate of street-community cultural facilities is 69, and the overall evaluation score is 70. It shows that there are significant differences in the actual use of cultural facilities, so it is particularly important to eliminate the differences in the use of cultural facilities between urban and rural areas. In the exploration of the law of urban spatial morphology evolution, it is found that the number of college students, total real estate investment, urban population, and total commercial housing sales have a significant impact on urban spatial expansion and evolution.

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Section: Introductionmentioning

confidence: 99%

Construction and Analysis of Urban Cultural Plane Space Mode considering Particle Swarm Cultural Scientific Computing Algorithm

2022

Security and Communication Networks

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“…Cyclic pursuit-based strategy was presented in [92] to achieve the distributed cooperative moving-target capturing in 3D space. Further, [66] proposed a novel cooperative control technique based on cyclic pursuit strategy combined with fuzzy PD control to accomplish the circular formation configuration of MAS in 3D space.…”

Section: Circular Formation Based Cyclic Pursuit Strategymentioning

confidence: 99%

“…A detailed analysis of CFP with relevance to measurements of agent's data such as position [48], distance [49,50], displacement [51,52], bearing (angel/position) [53,54], range [55], and received signal strength (RSS) [2] measurements opened new research paradigms. Presentation of a method-based classification of CFP, such as behavior-based method [56][57][58], leader-follower method [59,60], virtual structure method [61], artificial potential method [62][63][64], and the cyclic pursuit method [65][66][67] using the algebraic graph topology are the main context of this paper. The solution of CFP under different communication constraints such as the input saturation [68,69], disturbances in system dynamics [70], and locomotion constraint [40] are of extreme significance with respect to practical applications.…”

Section: Introductionmentioning

confidence: 99%

A Survey on Techniques in the Circular Formation of Multi-Agent Systems

2021

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Distributed control solutions to the multi-agent systems in terms of coordination, formation, and consensus problems are extensively studied in the literature. The circular formation control of multi-agent systems becomes one of the most challenging and active research topics in distributed cooperative control. The research potential of circular formation control has grown especially in satellite formation and control. The circular formation control aims to drive a group of agents to move on a circular trajectory around a center with spacing adjustment to avoid the collision. This paper covers the fundamental research in consensus and formation control and the latest developments in the applications of formation control especially in circular formation control and stability analysis. Literature related to various control streams to achieve circular formation is discussed. Algebraic graph theory is used as a building block in problem definition, problem formulation, and solutions. A review of bearing, range, and received signal strength measurements with respect to circular formation control is presented. Leader following, behavior-based, artificial potential function, virtual structure, and cyclic pursuit are the methods discussed most in this context. The stabilization of the collective circular motion with the unicycles approach is also discussed. Further results on circular formation, several applications, and future research directions are presented.

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“…This strategy is frequently used in cases where multiple agents must move in a coordinated manner, for instance, to entrap a target, conduct patrolling and surveillance operations, and seek sources in a sensed environment (see [1] and the references therein). The method-based classification of schemes implemented in circular formation control is as follows: leader-follower method [2][3][4], artificial potential method [5], behavior-based method [6,7], virtual structure method [8,9], event-triggered control method [10], and cyclic pursuit method [11][12][13][14][15][16][17][18][19][20]. Among these schemes, the cyclic pursuit method has attracted considerable attention because of its intrinsic benefits resulting from its minimal communication requirements for connectivity.…”

Section: Introductionmentioning

confidence: 99%

Graphical and Analytical Approaches for Analyzing Collective Behavior of Dynamic Multi-Agent Systems Governed by Generalized Cyclic Pursuit Mechanism

Kwak,

Kim,

Hori

et al. 2023

IEEE Access

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In this study, we use graphical and analytical methods to study the collective behavior of a swarm of dynamic agents controlled by the generalized cyclic pursuit (GCP) scheme. The proposed graphical approach reveals the occurrence mechanisms of various formation patterns in terms of agents' dynamics and tuning parameters of the GCP scheme, including the rendezvous, diverging spirals, circular pattern, and spirograph-like patterns. Then, we develop an analytical tool to obtain the crucial information needed to formulate expressions of the circular and spirograph-like steady-state trajectories generated by the GCP-controlled dynamic multi-agent system. In addition, the steady-state formation trajectories of a swarm of dynamic agents are formulated by explicitly considering the agents' general continuous-time linear timeinvariant model and the scenario in which more than two pairs of closed-loop poles exist on the imaginary axis of the complex plane. Numerical simulation cases of the GCP-controlled dynamic multi-agent system are presented to demonstrate the effectiveness of the proposed analysis methodology.

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Cyclic Pursuit-Fuzzy PD Control Method for Multi-agent Formation Control in 3D Space

Cited by 9 publications

References 13 publications

Construction and Analysis of Urban Cultural Plane Space Mode considering Particle Swarm Cultural Scientific Computing Algorithm

Construction and Analysis of Urban Cultural Plane Space Mode considering Particle Swarm Cultural Scientific Computing Algorithm

A Survey on Techniques in the Circular Formation of Multi-Agent Systems

Graphical and Analytical Approaches for Analyzing Collective Behavior of Dynamic Multi-Agent Systems Governed by Generalized Cyclic Pursuit Mechanism

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