Indirect Optimal Control of Advection-Diffusion Fields through Distributed Robotic Swarms

Carlo Sinigaglia, Andrea Manzoni, Francesco Braghin, Spring Berman

Research output: Contribution to journalConference articlepeer-review


We consider the problem of optimally guiding a large-scale swarm of underwater vehicles that is tasked with the indirect control of an advection-diffusion environmental field. The microscopic vehicle dynamics are governed by a stochastic differential equation (SDE) with drift. The drift terms model the self-propelled velocity of the vehicle and the velocity field of the currents. In the mean-field setting, the macroscopic vehicle dynamics are governed by a Kolmogorov forward equation in the form of a linear parabolic advection-diffusion partial differential equation (PDE). The environmental field is governed by an advection-diffusion PDE in which the advection term is defined by the fluid velocity field. The vehicles are equipped with on-board actuators that enable the swarm to act as a distributed source in the environmental field, modulated by a scalar control parameter that determines the local source intensity. In this setting, we formulate an optimal control problem to compute the vehicle velocity and actuator intensity fields that drive the environmental field to a desired distribution within a specified amount of time. After proving an existence result for the solution of the optimal control problem, we discretize and solve the problem using the Finite Element Method (FEM). We show through numerical simulations the effectiveness of our control strategy in regulating the environmental field to zero or to a desired distribution in the presence of a double-gyre flow field.

Original languageEnglish (US)
Pages (from-to)299-304
Number of pages6
Issue number30
StatePublished - 2022
Event25th IFAC Symposium on Mathematical Theory of Networks and Systems, MTNS 2022 - Bayreuthl, Germany
Duration: Sep 12 2022Sep 16 2022


  • advection-diffusion equation
  • coupled PDEs
  • indirect control
  • mean-field modeling
  • Optimal control
  • swarm robotics
  • underwater vehicles

ASJC Scopus subject areas

  • Control and Systems Engineering


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