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Distributed model predictive control approach for cooperative car-following with guaranteed local and string stability

机译:保证局部和弦稳定性的协同汽车跟随的分布式模型预测控制方法

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摘要

In this paper, a serial distributed model predictive control (MPC) approach for connected automated vehicles (CAVS) is developed with local stability (disturbance dissipation over time) and multi-criteria string stability (disturbance attenuation through a vehicular string). Two string stability criteria are considered within the proposed MPC: (i) the l(infinity)-norm string stability criterion for attenuation of the maximum disturbance magnitude and (ii) l(2)-norm string stability criterion for attenuation of disturbance energy. The l(infinity)-norm string stability is achieved by formulating constraints within the MPC based on the future states of the leading CAV, and the l(2)-norm string stability is achieved by proper weight matrix tuning over a robust positive invariant set. For rigor, mathematical proofs for asymptotical local stability and multi-criteria string stability are provided. Simulation experiments verify that the distributed serial MPC proposed in this study is effective for disturbance attenuation and performs better than traditional MPC without stability guarantee. Published by Elsevier Ltd.
机译:在本文中,开发了一种用于连接自动驾驶汽车(CAVS)的串行分布式模型预测控制(MPC)方法,该方法具有局部稳定性(随时间推移的干扰耗散)和多准则字符串的稳定性(通过车辆琴弦的干扰衰减)。在建议的MPC中考虑了两个弦稳定性标准:(i)用于最大干扰幅度衰减的l(无穷大)规范弦稳定性准则,以及(ii)用于干扰能量衰减的l(2)-规范弦稳定性准则。 l(无穷)-范数字符串的稳定性是通过根据领先的CAV的未来状态在MPC中制定约束条件来实现的,而l(2)-范数字符串的稳定性是通过对鲁棒的正不变集进行适当的权重矩阵调整来实现的。为严格起见,提供了关于渐近局部稳定性和多准则字符串稳定性的数学证明。仿真实验证明,本文提出的分布式串行MPC可以有效地抑制干扰,并且在没有稳定性保证的情况下性能优于传统的MPC。由Elsevier Ltd.发布

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