Abstract
This paper investigates the resource-aware density regulation problem for a large-scale robotic swarm. A perturbed mean-field model(MFM) is first developed to describe the evolution process of the swarm’s actual density distribution (ADD) in a macroscopic manner, thus endowing the control algorithm with scalability property. A novel event-triggered (ET) model predictive mean-field control (MFC) algorithm is proposed to reduce the computation and communication burdens of agents while providing high control performance. Finally, by means of the numerical example, we verify the effectiveness of this algorithm.
This work was supported in part by the National Natural Science Foundation of China (NSFC) under Grant U22B2039, 62273281; in part by Aoxiang Youth Scholar Program under Grant.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Elamvazhuthi, K., Kakish, Z., Shirsat, A., et al.: Controllability and stabilization for herding a robotic swarm using a leader: a mean-field approach. IEEE Trans. Rob. 37(2), 418–432 (2021)
Bono, A., Fedele, G., Franze, G.: A swarm-based distributed model predictive control scheme for autonomous vehicle formations in uncertain environments. IEEE Trans. Cybern. (2021). https://doi.org/10.1109/TCYB.2021.3070461
La, H., Nguyen, T., Le, T., Jafari, M.: Formation control and obstacle avoidance of multiple rectangular agents with limited communication ranges. IEEE Trans. Control Netw. Syst. 4(4), 680–691 (2017)
Peng, Z., Wang, J., Wang, D.: Distributed maneuvering of autonomous surface vehicles based on neurodynamic optimization and fuzzy approximation. IEEE Trans. Control Syst. Technol. 26(3), 1083–1090 (2018)
Bandyopadhyay, S., Chung, S., Hadaegh, F.Y.: Probabilistic and distributed control of a large-scale swarm of autonomous agents. IEEE Trans. Robot. 33(5), 3896–3901 (2009)
Elamvazhuthi, K., Berman, S.: Mean-field models in swarm robotics: a survey. Bioinspiration Biomimetics 15(1), 015001 (2019)
Chattopadhyay, I., Ray, A.: Supervised self-organization of homogeneous swarms using ergodic projections of Markov chains. IEEE Trans. Syst. Man, Cybern. Part B (Cybernetics) 39(6), 1505–1515 (2009)
Açikmeşe, B., Bayard, D.S.: A Markov chain approach to probabilistic swarm guidance. In: 2012 American Control Conference (ACC), pp. 6300–6307. IEEE, Canada (2012)
Billera, L.J., Diaconis, P.: A geometric interpretation of the Metropolis-Hastings algorithm. Stat. Sci. 16(4), 335–339 (2001)
Hsieh, M.A., Halasz, A., Berman, S., Kumar, V.: Biologically inspired redistribution of a swarm of robots among multiple sites. Swarm Intell. 2, 121–141 (2008)
Zheng, T., Han, Q., Lin, H.: Transporting robotic swarms via mean-field feedback control. IEEE Trans. Autom. Control 67(8), 4170–4177 (2022)
Bandyopadhyay, S., Chung, S.-J., Hadaegh, F.Y.: Probabilistic swarm guidance using optimal transport. In: 2014 IEEE Conference on Control Applications (CCA), pp. 498–505. IEEE, France (2014)
Bandyopadhyay, S., Chung, S.-J., Hadaegh, F.Y.: Probabilistic and distributed control of a large-scale swarm of autonomous agents. IEEE Trans. Rob. 33(5), 1103–1123 (2017)
Chen, H., Allgöwer, F.: A quasi-infinite horizon nonlinear model predictive control scheme with guaranteed stability. Automatica 34(10), 1205–1217 (1998)
Zhu, Y., Ozguner, U.: Robustness analysis on constrained model predictive control for nonholonomic vehicle regulation. In: Proceedings of American Control Conference, pp. 1103-1123. IEEE, USA (2009)
Michalska, H., Mayne, D.Q.: Robust recedindg horizon control of constrained nonlinear systems. IEEE Trans. Autom. Control 38(11), 1623–1633 (1993)
Zou, Y., Su, X., Li, S., Niu, Y., Li, D.: Event-triggered distributed predictive control for asynchronous coordination of multi-agent systems. Automatica 99, 92–98 (2019)
Li, H., Yan, W., Shi, Y., Wang, Y.: Periodic event-triggering in distributed receding horizon control of nonlinear systems. Syst. Control Lett. 86, 16–23 (2015)
Cui, D., Li, H.: Dual self-triggered model-predictive control for nonlinear cyber-physical systems. IEEE Trans. Syst. Man, Cybern.: Syst. 52(6), 3442–3452 (2022)
Eqtami, A., Heshmati-alamdari, S., Dimarogonas, D.V., Kyriakopoulos, K.J.: Self-triggered model predictive control for nonholonomic systems. In: Proceedings of the European Control Conference, pp. 638–643. IEEE, Switzerland (2013)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2023 The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.
About this paper
Cite this paper
Cui, D., Li, H., Huang, P. (2023). Event-Triggered Model Predictive Mean-Field Control for Stabilizing Robotic Swarm. In: Yang, H., et al. Intelligent Robotics and Applications. ICIRA 2023. Lecture Notes in Computer Science(), vol 14273. Springer, Singapore. https://doi.org/10.1007/978-981-99-6498-7_43
Download citation
DOI: https://doi.org/10.1007/978-981-99-6498-7_43
Published:
Publisher Name: Springer, Singapore
Print ISBN: 978-981-99-6497-0
Online ISBN: 978-981-99-6498-7
eBook Packages: Computer ScienceComputer Science (R0)