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

机译:基于协作协同进化的多机器人编队模型预测控制

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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.
机译:本文提出了一种基于合作协同进化(CC)的模型预测控制(MPC)来解决地层控制问题。尽管基于常规进化算法(EA)的MPC方法不能保证稳定性,但无论基于CC的算法生成的解决方案的最优性如何,使用基于CC的MPC都能保证渐近稳定性。为了保证稳定性,找到终端状态约束,然后对所有候选解决方案应用修复算法来满足该约束。基于CC的EA中最新开发的算法-协同协同进化粒子群算法(CCPSO)被采用并进行了修改,以适应地层控制问题。在一组非完整的移动机器人上进行了实验,以证明基于CC的MPC的有效性。

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