The synthesis of control laws for interacting agent-based dynamics and theirmean-field limit is studied. A linearization-based approach is used for thecomputation of sub-optimal feedback laws obtained from the solution ofdifferential matrix Riccati equations. Quantification of dynamic performance ofsuch control laws leads to theoretical estimates on suitable linearizationpoints of the nonlinear dynamics. Subsequently, the feedback laws are embeddedinto nonlinear model predictive control framework where the control is updatedadaptively in time according to dynamic information on moments of linearmean-field dynamics. The performance and robustness of the proposed methodologyis assessed through different numerical experiments in collective dynamics.

Moment-Driven Predictive Control of Mean-Field Collective Dynamics

G. Albi;C. Segala
2022

Abstract

The synthesis of control laws for interacting agent-based dynamics and theirmean-field limit is studied. A linearization-based approach is used for thecomputation of sub-optimal feedback laws obtained from the solution ofdifferential matrix Riccati equations. Quantification of dynamic performance ofsuch control laws leads to theoretical estimates on suitable linearizationpoints of the nonlinear dynamics. Subsequently, the feedback laws are embeddedinto nonlinear model predictive control framework where the control is updatedadaptively in time according to dynamic information on moments of linearmean-field dynamics. The performance and robustness of the proposed methodologyis assessed through different numerical experiments in collective dynamics.
Mathematics - Optimization and Control
Mathematics - Optimization and Control
Computer Science - Numerical Analysis
Mathematics - Numerical Analysis
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Utilizza questo identificativo per citare o creare un link a questo documento: http://hdl.handle.net/11562/1059915
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