Abstract
This paper proposes a Nash equilibrium-based model predictive control (MPC) scheme incorporating a cooperative particle swarm optimization (CPSO) to deal with the control of flocking robots whose state vectors are coupled in a cost function. In conventional distributed MPC, the stability is assured by guaranteeing a bounded error between what a subsystem plans to do and what neighbors believe that the subsystem plans to do over a finite prediction horizon. This condition is referred to as compatibility constraint, and the closed-loop control performance largely depends on the responses computed at the previous time step. As an alternative of the compatibility constraint, the distributed CPSO is suggested in an MPC framework, which guarantees the stability without enforcing the compatibility constraint. A numerical simulation is performed on a group of nonholonomic mobile robots to demonstrate the effectiveness of the proposed MPC scheme incorporating CPSO.
Access provided by Autonomous University of Puebla. Download to read the full chapter text
Chapter PDF
Similar content being viewed by others
Keywords
References
Dunbar, W.B., Murray, R.M.: Distributed receding horizon control for multi-vehicle formation stabilization. Automatica 42(4), 549–558 (2006)
Dunbar, W.B., Caveney, D.S.: Distributed receding horizon control of vehicle platoons: stability and string stability. IEEE Trans. Autom. Control 57(3), 620–633 (2012)
Fontes, F.A.C.C.: A general framework to design stabilizing nonlinear model predictive controllers. Syst. Control Lett. 42, 127–143 (2001)
Keviczky, T., Borrelli, F., Balas, G.J.: Decentralized receding horizon control for large scale dynamically decoupled systems. Automatica 42(12), 2105–2115 (2006)
Gu, D., Hu, H.: A stabilizing receding horizon regulator for nonholonomic mobile robots. IEEE Trans. Robot. 21(5), 1022–1028 (2005)
Chen, J., Sun, D., Yang, J., Chen, H.: Leader-follower formation control of multiple non-holonomic mobile robots incorporating a receding-horizon scheme. Int. J. Robot. Res. 29(6), 727–747 (2010)
van den Bergh, F., Engelbrecht, A.: A cooperative approach to particle swarm optimization. IEEE Trans. Evol. Comput. 8(3), 225–239 (2004)
Li, X., Yao, X.: Tackling high dimensional nonseparable optimization problems by cooperatively coevolving particle swarms. In: Proc. IEEE Congress on Evolutionary Computation (CEC), pp. 1546–1553 (2009)
Li, X., Yao, X.: Cooperatively coevolving particle swarms for large scale optimization. IEEE Trans. Evol. Comput. 16(2), 210–224 (2012)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2013 Springer-Verlag Berlin Heidelberg
About this chapter
Cite this chapter
Lee, SM., Myung, H. (2013). Particle Swarm Optimization-Based Distributed Control Scheme for Flocking Robots. In: Kim, JH., Matson, E., Myung, H., Xu, P. (eds) Robot Intelligence Technology and Applications 2012. Advances in Intelligent Systems and Computing, vol 208. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-37374-9_50
Download citation
DOI: https://doi.org/10.1007/978-3-642-37374-9_50
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-37373-2
Online ISBN: 978-3-642-37374-9
eBook Packages: EngineeringEngineering (R0)