Bilinear Controllability of a Class of Advection-Diffusion-Reaction Systems

Karthik Elamvazhuthi, H. J. Kuiper, Matthias Kawski, Spring Berman

Research output: Contribution to journalArticlepeer-review

17 Scopus citations

Abstract

In this paper, we investigate the exact controllability properties of an advection-diffusion equation on a bounded domain, using time-and space-dependent velocity fields as the control parameters. This partial differential equation (PDE) is the Kolmogorov forward equation for a reflected diffusion process that models the spatiotemporal evolution of a swarm of agents. We prove that if a target probability density has bounded first-order weak derivatives and is uniformly bounded from below by a positive constant, then it can be reached in finite time using control inputs that are bounded in space and time. We then extend this controllability result to a class of advection-diffusion-reaction PDEs that corresponds to a hybrid switching diffusion process (HSDP), in which case the reaction parameters are additionally incorporated as the control inputs. For the HSDP, we first constructively prove controllability of the associated continuous-time Markov chain (CTMC) system in which the state space is finite. Then, we show that our controllability results for the advection-diffusion equation and the CTMC can be combined to establish controllability of the forward equation of the HSDP. Finally, we provide constructive solutions to the problem of asymptotically stabilizing an HSDP to a target nonnegative stationary distribution using time-independent state feedback laws, which correspond to spatially dependent coefficients of the associated system of PDEs.

Original languageEnglish (US)
Article number8561138
Pages (from-to)2282-2297
Number of pages16
JournalIEEE Transactions on Automatic Control
Volume64
Issue number6
DOIs
StatePublished - Jun 2019

Keywords

  • Advection-diffusion-reaction partial differential equation (PDE)
  • continuous-time Markov chains (CTMCs)
  • controllability
  • stochastic processes
  • swarm robotics

ASJC Scopus subject areas

  • Control and Systems Engineering
  • Computer Science Applications
  • Electrical and Electronic Engineering

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