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Cooperative coevolution-based model predictive control for multi-robot formation

Seung‐Mok Lee, Hanguen Kim, Hyun Myung

Year
2013
Citations
4

Abstract

In this paper, a novel cooperative coevolution (CC)-based model predictive control (MPC) is proposed to deal with formation control problem. While conventional evolutionary algorithm (EA)-based MPC approaches cannot guarantee the stability, the proposed CC-based MPC guarantees the asymptotic stability regardless of the optimality of the solution that the CC-based algorithm generates with a small number of individuals. To guarantee the stability, a terminal state constraint is found, and then a repair algorithm is applied to all candidate solutions to meet the constraint. The cooperatively coevolving particle swarm optimization (CCPSO), most recently developed algorithm among CC-based EAs, is adopted and modified to fit into the formation control problem. Experiments are performed on a group of nonholonomic mobile robots to demonstrate the effectiveness of the CC-based MPC.

Keywords

Model predictive controlStability (learning theory)Constraint (computer-aided design)Mathematical optimizationCoevolutionMobile robotComputer scienceParticle swarm optimizationNonholonomic systemExponential stability

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