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Cooperative Coevolution-based Model Predictive Control for Multi-Robot Formation

机译:基于协作的基于协作的多机器人形成模型预测控制

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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:在本文中,提出了一种新颖的协同协会(CC)的模型预测控制(MPC)来处理形成控制问题。虽然传统的进化算法(EA)基于MPC方法不能保证稳定性,但是所提出的基于CC的MPC保证了渐近稳定性,而不管CC的算法产生少量个体的解决方案的最优性。为了保证稳定性,找到终端状态约束,然后将修复算法应用于所有候选解决方案以满足约束。采用并修改基于CC基于CC基于CC的EAS中最近开发的算法的协同辅助粒子群优化(CCPSO)以适应地层控制问题。实验是对一组非完整的移动机器人进行的,以证明基于CC的MPC的有效性。

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