Technical communique: Stabilizing decentralized model predictive control of nonlinear systems

Authors:
L. Magni;R. Scattolini
Affiliations:
Dipartimento di Informatica e Sistemistica, Universití degli Studi di Pavia, via Ferrata 1, 27100 Pavia, Italy;Dipartimento di Elettronica e Informazione, Politecnico di Milano, p.za Leonardo da Vinci, 32, 20133 Milano, Italy
Venue:
Automatica (Journal of IFAC)
Year:
2006

Citing 3
Cited 7

Discrete-time stability with perturbations: application to model predictive control

Automatica (Journal of IFAC)
Survey Constrained model predictive control: Stability and optimality

Automatica (Journal of IFAC)
Brief Decentralized disturbance attenuating output-feedback trackers for large-scale nonlinear systems

Automatica (Journal of IFAC)

Distributed model predictive control of nonlinear systems subject to asynchronous and delayed measurements

Automatica (Journal of IFAC)
Modeling of gauge predictive control system in hot rolling mill based on BP network

ASID'09 Proceedings of the 3rd international conference on Anti-Counterfeiting, security, and identification in communication
Brief paper: Distributed model predictive control of dynamically decoupled systems with coupled cost

Automatica (Journal of IFAC)
Brief paper: Set membership approximation of discontinuous nonlinear model predictive control laws

Automatica (Journal of IFAC)
Distributed predictive control: A non-cooperative algorithm with neighbor-to-neighbor communication for linear systems

Automatica (Journal of IFAC)
Cooperative distributed MPC for tracking

Automatica (Journal of IFAC)
On distributed constrained formation control in operator-vehicle adversarial networks

Automatica (Journal of IFAC)

Quantified Score

Hi-index	22.16

Visualization

Abstract

This note presents a stabilizing decentralized model predictive control (MPC) algorithm for nonlinear discrete time systems. No information is assumed to be exchanged between local control laws. The stability proof relies on the inclusion of a contractive constraint in the formulation of the MPC problem.