Stochastic MPC Using the Unscented Transform

Torben Knudsen; John Leth

doi:10.23919/ACC.2018.8430903

Stochastic MPC Using the Unscented Transform

Research output: Contribution to book/anthology/report/conference proceeding › Article in proceeding › Research › peer-review

2 Citations (Scopus)

Abstract

Model predictive control for linear stochastic systems with constraints has previously been solved using so called scenario methods which gives an approximate solution which is very computationally demanding. Here the model predictive control problem for linear stochastic system with known fixed system matrices are considered. Only outputs with measurement noise is assumed measurable. The main contribution is the development of a deterministic convex standard MPC problem which approximate the solution to the stochastic MPC problem.

Original language	English
Title of host publication	2018 Annual American Control Conference, ACC 2018
Number of pages	7
Publisher	IEEE
Publication date	9 Aug 2018
Pages	4718-4724
Article number	8430903
ISBN (Print)	978-1-5386-5429-3
ISBN (Electronic)	978-1-5386-5428-6
DOIs	https://doi.org/10.23919/ACC.2018.8430903
Publication status	Published - 9 Aug 2018
Event	2018 Annual American Control Conference, ACC 2018 - Milwauke, United States Duration: 27 Jun 2018 → 29 Jun 2018

Conference

Conference	2018 Annual American Control Conference, ACC 2018
Country/Territory	United States
City	Milwauke
Period	27/06/2018 → 29/06/2018

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10.23919/ACC.2018.8430903

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title = "Stochastic MPC Using the Unscented Transform",

abstract = "Model predictive control for linear stochastic systems with constraints has previously been solved using so called scenario methods which gives an approximate solution which is very computationally demanding. Here the model predictive control problem for linear stochastic system with known fixed system matrices are considered. Only outputs with measurement noise is assumed measurable. The main contribution is the development of a deterministic convex standard MPC problem which approximate the solution to the stochastic MPC problem.",

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AB - Model predictive control for linear stochastic systems with constraints has previously been solved using so called scenario methods which gives an approximate solution which is very computationally demanding. Here the model predictive control problem for linear stochastic system with known fixed system matrices are considered. Only outputs with measurement noise is assumed measurable. The main contribution is the development of a deterministic convex standard MPC problem which approximate the solution to the stochastic MPC problem.

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U2 - 10.23919/ACC.2018.8430903

DO - 10.23919/ACC.2018.8430903

M3 - Article in proceeding

AN - SCOPUS:85052551893

SN - 978-1-5386-5429-3

SP - 4718

EP - 4724

BT - 2018 Annual American Control Conference, ACC 2018

PB - IEEE

T2 - 2018 Annual American Control Conference, ACC 2018

Y2 - 27 June 2018 through 29 June 2018

ER -

Stochastic MPC Using the Unscented Transform

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