Bounded consensus of double-integrator stochastic multi-agent systems

Mei Luo; Jinrong Wang; Yumei Liao

doi:10.3934/dcdss.2022088

Article Contents

2022, Volume 15, Issue 11: 3243-3260. Doi: 10.3934/dcdss.2022088

This issue Previous Article A data-driven approach to the evaluation of asphalt pavement structures using falling weight deflectometer Next Article Observer-based SMC for stochastic systems with disturbance driven by fractional Brownian motion

Bounded consensus of double-integrator stochastic multi-agent systems

1.
Department of Mathematics, Guizhou University, Guiyang, Guizhou 550025, China
2.
Department of Mathematics, Guizhou Education University, Guiyang, Guizhou 550018, China

^* Corresponding author: Jinrong Wang
^* Corresponding author: Jinrong Wang

Received: September 2021

Revised: February 2022

Early access: April 2022

Published: September 2022

Abstract Full Text(HTML) Figure(5) / Table(4) Related Papers Cited by

Abstract

In the framework of fixed topology and stochastic switching topologies, we study the mean-square bounded consensus(MSBC) of double-integrator stochastic multi-agent systems(SMASs) including additive system noises and communication noises. Combining algebra, graph theory and random analysis, we obtain several equivalent conditions for double-integrator SMASs to reach MSBC. In addition, the simulation examples also verify the correctness of the theoretical results.

Keywords:

Double-integrator stochastic multi-agent systems,
noises,
stochastic switching topologies,
mean-square bounded consensus.

Mathematics Subject Classification: 60H10, 93D50.

Citation:

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Figure 1. Topology $ \mathcal{G} $

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Figure 2. The state trajectories

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Figure 3. Network topology $ \{\mathcal{G}_{1}, \mathcal{G}_{2}, \mathcal{G}_{3}\} $

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Figure 4. The topological transformation diagram

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Figure 5. The state trajectories

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Table 1. The position of each agent

Agent	0	10	20	30	40	50
1	8	98.8028	126.8127	154.6091	179.8284	203.3477
2	2	80.8029	121.4406	153.7326	180.2018	204.0143
3	5	90.3149	124.2652	154.2146	180.1108	203.6502
4	4	87.0743	123.1808	153.3275	179.3758	203.0953
5	10	78.4122	120.9336	153.4453	180.1209	203.6704
6	6	93.0754	125.0095	153.7932	179.5006	202.9661

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Table 2. The velocity of each agent

Agent	0	10	20	30	40	50
1	80	2.4374	2.8897	2.8699	2.2523	2.6406
2	20	4.5277	3.5997	3.0241	2.2809	2.5409
3	50	3.3306	3.2226	2.9195	2.2264	2.5746
4	40	3.6902	3.3191	3.0047	2.2885	2.5710
5	10	4.8084	3.6863	2.9844	2.2629	2.5924
6	60	2.9621	3.0973	2.8919	2.2493	2.6171

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Table 3. The position of each agent

Agent	0	20	40	60	80	100	120
1	1	28.5681	50.5773	72.6362	94.7570	116.3468	138.3723
2	2	29.2191	50.7588	73.2699	95.6504	115.7632	138.0114
3	3	33.0884	49.7954	73.5687	96.0563	115.5744	137.8249
4	4	29.7302	50.4890	73.1723	95.2234	115.8630	138.2307
5	5	34.3277	48.9584	73.0675	94.9233	116.1680	138.3576

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Table 4. The velocity of each agent

Agent	0	20	40	60	80	100	120
1	6	1.1000	1.1000	1.1000	1.1000	1.1000	1.1000
2	5	0.9616	1.1125	1.1042	1.1115	1.1478	1.0758
3	2	0.4320	1.1781	1.1128	1.0867	1.1135	1.1100
4	4	0.7428	1.1314	1.1051	1.1116	1.1860	1.0625
5	0	0.2022	1.1915	1.1197	1.0935	1.1285	1.0718

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