Moment-Driven Predictive Control of Mean-Field Collective Dynamics
Moment-Driven Predictive Control of Mean-Field Collective Dynamics
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DOI：
10.1137/21m1391559
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发表时间：
2021-01
期刊：
影响因子：
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通讯作者：
G. Albi;M. Herty;D. Kalise;C. Segala
中科院分区：
文献类型：
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作者：
G. Albi;M. Herty;D. Kalise;C. Segala
研究了基于相互作用的智能体动力学的控制律的综合及其平均场极限。采用基于线性化的方法计算由求解微分矩阵Riccati方程得到的次最优反馈律。这种控制律的动态性能的量化导致了对非线性动态的适当线性化点的理论估计。随后，将反馈律嵌入到非线性模型预测控制框架中，根据线性平均场动态矩的动态信息及时自适应地更新控制。通过集体动力学的不同数值实验评估了所提出方法的性能和稳健性。
The synthesis of control laws for interacting agent-based dynamics and their mean-field limit is studied. A linearization-based approach is used for the computation of sub-optimal feedback laws obtained from the solution of differential matrix Riccati equations. Quantification of dynamic performance of such control laws leads to theoretical estimates on suitable linearization points of the nonlinear dynamics. Subsequently, the feedback laws are embedded into nonlinear model predictive control framework where the control is updated adaptively in time according to dynamic information on moments of linear mean-field dynamics. The performance and robustness of the proposed methodology is assessed through different numerical experiments in collective dynamics.