Output disturbance rejection using parallel model predictive control

The solution time of the online optimization problems inherent to Model Predictive Control (MPC) can become a critical limitation when working in embedded systems. One proposed approach to reduce the solution time is to split the optimization problem into a number of reduced order problems, solve su...

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Bibliographic Details
Published in2013 XXIV International Conference on Information, Communication and Automation Technologies (ICAT) pp. 1 - 7
Main Authors Andrade-Cabrera, Carlos, Maciejowski, Jan M.
Format Conference Proceeding
LanguageEnglish
Published IEEE 01.10.2013
Subjects
Aircraft
Asymptotic stability
Atmospheric modeling
Multiplexing
Predictive control
Trajectory
Vectors
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Summary:The solution time of the online optimization problems inherent to Model Predictive Control (MPC) can become a critical limitation when working in embedded systems. One proposed approach to reduce the solution time is to split the optimization problem into a number of reduced order problems, solve such reduced order problems in parallel and selecting the solution which minimises a global cost function. This approach is known as Parallel MPC. The potential capabilities of disturbance rejection are introduced using a simulation example. The algorithm is implemented in a linearised model of a Boeing 747-200 under nominal flight conditions and with an induced wind disturbance. Under significant output disturbances Parallel MPC provides a significant improvement in performance when compared to Multiplexed MPC (MMPC) and Linear Quadratic Synchronous MPC (SMPC).
DOI:10.1109/ICAT.2013.6684069