Stabilizing output feedback nonlinear model predictive control : an extended observer approach

Research output: Chapter in Book/Report/Conference proceedingConference contributionAcademicpeer-review

2 Downloads (Pure)


Abstract—Nonlinear Model Predictive Control (NMPC), generally based on nonlinear state space models, needs knowledge of the full state for feedback. However, in practice knowledge of the full state is usually not available. Therefore, an asymptotically stabilizing MPC scheme for a class of nonlinear discrete-time systems is proposed, which only requires knowledge of the output of the system for feedback. The presented output based NMPC scheme consists of an extended observer interconnected with an NMPC controller which represents a possibly discontinuous state feedback control law. Sufficient conditions for asymptotic stability of the system in closed-loop with the NMPC observer interconnection are derived using the discrete-time input-tostate stability framework. Moreover, it is shown that there always exist NMPC tuning parameters and observer gains, such that the derived sufficient stabilization conditions can be satisfied.
Original languageEnglish
Title of host publicationProceedings of the 17th Symposium on Mathematical Theory for Networks and Systems(MTNS2006) 24-28 July 2006, Kyoto, Japan
Place of PublicationKyoto, Japan
Publication statusPublished - 2006


Dive into the research topics of 'Stabilizing output feedback nonlinear model predictive control : an extended observer approach'. Together they form a unique fingerprint.

Cite this