Disturbance rejection in robust PdE-based MRAC laws for uncertain heterogeneous multiagent networks under boundary reference

Jun Kim, Vivek Natarajan, Scott David Kelly, Joseph Bentsman

Research output: Contribution to journalArticlepeer-review


Disturbance is a pervasive source of uncertainty in most applications. This paper presents model reference adaptive control (MRAC) laws for uncertain multiagent networks with a disturbance rejection capability. The algorithms proposed can also be viewed as the extension of the robust model reference adaptive control (MRAC) laws with disturbance rejection recently derived for systems described by parabolic and hyperbolic partial differential equations (PDEs) with spatially-varying parameters under distributed sensing and actuation to heterogeneous multiagent networks characterized by parameter uncertainty. The latter extension is carried out using partial difference equations (PdEs) on graphs that preserve parabolic and hyperbolic like cumulative network behavior. Unlike in the PDE case, only boundary input is specified for the reference model. The algorithms proposed directly incorporate this boundary reference input into the reference PdE to generate the distributed admissible reference evolution profile followed by the agents. The agent evolution thus depends only on the interaction with the adjacent agents, making the system fully decentralized. Numerical examples are presented as well. The resulting PdE MRAC laws inherit the robust linear structure of their PDE counterparts.

Original languageEnglish (US)
Pages (from-to)484-495
Number of pages12
JournalNonlinear Analysis: Hybrid Systems
Issue number3
StatePublished - Aug 2010


  • Decentralized control
  • Disturbance rejection
  • Model reference adaptive control
  • Parameter uncertainty

ASJC Scopus subject areas

  • Control and Systems Engineering
  • Analysis
  • Computer Science Applications


Dive into the research topics of 'Disturbance rejection in robust PdE-based MRAC laws for uncertain heterogeneous multiagent networks under boundary reference'. Together they form a unique fingerprint.

Cite this