State space files for the control101 toolbox

J. A. Rossiter; R. Drummond; L. Su; R. Bars

doi:10.1016/j.ifacol.2025.08.022

State space files for the control101 toolbox

J. A. Rossiter
, R. Drummond
, L. Su
, R. Bars

EEE - Academic & Research

Research output: Contribution to journal › Conference article › peer-review

Abstract

It is widely recognised (Rossiter et al., 2020) that state space methods are fundamental to control engineers, even if not included in an introductory control course due to space restrictions. Consequently, it makes sense for the control101 toolbox (Rossiter, 2024) to include state space resources, to support those places where this is considered fundamental. In view of this, the authors have been developing a set of resources to support an introduction to state space methods in control and this paper gives an overview of those resources (released in late summer 2024), alongside some supporting background.

Original language	English
Pages (from-to)	54-59
Number of pages	6
Journal	IFAC-PapersOnLine
Volume	59
Issue number	7
DOIs	https://doi.org/10.1016/j.ifacol.2025.08.022
Publication status	Published - 1 Jun 2025
Event	14th IFAC Symposium on Advances in Control Education, ACE 2025 - Budapest, Hungary Duration: 17 Jun 2025 → 21 Jun 2025

Keywords

Control101
Independent learning
state space models

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10.1016/j.ifacol.2025.08.022

Cite this

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title = "State space files for the control101 toolbox",

abstract = "It is widely recognised (Rossiter et al., 2020) that state space methods are fundamental to control engineers, even if not included in an introductory control course due to space restrictions. Consequently, it makes sense for the control101 toolbox (Rossiter, 2024) to include state space resources, to support those places where this is considered fundamental. In view of this, the authors have been developing a set of resources to support an introduction to state space methods in control and this paper gives an overview of those resources (released in late summer 2024), alongside some supporting background.",

keywords = "Control101, Independent learning, state space models",

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T1 - State space files for the control101 toolbox

AU - Rossiter, J. A.

AU - Drummond, R.

AU - Su, L.

AU - Bars, R.

PY - 2025/6/1

Y1 - 2025/6/1

N2 - It is widely recognised (Rossiter et al., 2020) that state space methods are fundamental to control engineers, even if not included in an introductory control course due to space restrictions. Consequently, it makes sense for the control101 toolbox (Rossiter, 2024) to include state space resources, to support those places where this is considered fundamental. In view of this, the authors have been developing a set of resources to support an introduction to state space methods in control and this paper gives an overview of those resources (released in late summer 2024), alongside some supporting background.

AB - It is widely recognised (Rossiter et al., 2020) that state space methods are fundamental to control engineers, even if not included in an introductory control course due to space restrictions. Consequently, it makes sense for the control101 toolbox (Rossiter, 2024) to include state space resources, to support those places where this is considered fundamental. In view of this, the authors have been developing a set of resources to support an introduction to state space methods in control and this paper gives an overview of those resources (released in late summer 2024), alongside some supporting background.

KW - Control101

KW - Independent learning

KW - state space models

UR - https://www.scopus.com/pages/publications/105016233126

U2 - 10.1016/j.ifacol.2025.08.022

DO - 10.1016/j.ifacol.2025.08.022

M3 - Conference article

AN - SCOPUS:105016233126

SN - 2405-8971

VL - 59

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EP - 59

JO - IFAC-PapersOnLine

JF - IFAC-PapersOnLine

IS - 7

T2 - 14th IFAC Symposium on Advances in Control Education, ACE 2025

Y2 - 17 June 2025 through 21 June 2025

ER -

State space files for the control101 toolbox

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Keywords

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