Particle swarm optimization-based distributed control scheme for flocking robots

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. © 2013 Springer-Verlag.