Density Control of Large-Scale Particles Swarm Through PDE-Constrained Optimization

We describe in this paper an optimal control strategy for shaping a large-scale swarm of particles using boundary global actuation. This problem arises as a key challenge in many swarm robotics applications, especially when the robots are passive particles that need to be guided by external control fields. The system is large-scale and underactuated, making the control strategy at the microscopic particle level infeasible. We consider the Kolmogorov forward equation associated to the stochastic process of the single particle to encode the macroscopic behaviour of the particles swarm. The control inputs shape the velocity field of the density dynamics according to the physical model of the actuators. We find the optimal actuation considering an optimal control problem whose state dynamics is governed by a linear parabolic advection-diffusion equation where the control induces a transport field. From a theoretical standpoint, we show the existence of a solution to the resulting nonlinear optimal control problem. From a numerical standpoint, we employ the discrete adjoint method to accurately compute the reduced gradient and we show how it commutes with the optimize-then-discretize approach. Finally, numerical simulations show the effectiveness of the control strategy in driving the density sufficiently close to the target.