Scheduling of Periodic Event-Triggered Control to balance Control Performance and Average Inter-Sample Times

Master Thesis (2023)
Author(s)

M.A.J. Looman (TU Delft - Mechanical Engineering)

Contributor(s)

M. Mazo – Mentor (TU Delft - Team Manuel Mazo Jr)

K. Batselier – Graduation committee member (TU Delft - Team Kim Batselier)

Faculty
Mechanical Engineering
Copyright
© 2023 Menno Looman
More Info
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Publication Year
2023
Language
English
Copyright
© 2023 Menno Looman
Graduation Date
29-08-2023
Awarding Institution
Delft University of Technology
Programme
['Mechanical Engineering | Systems and Control']
Related content

Link to the gitlab branch with all relevant code used and produced during this thesis project

http://gitlab.tudelft.nl/sync-lab/ETCetera/-/tree/menno_thesis
Faculty
Mechanical Engineering
Reuse Rights

Other than for strictly personal use, it is not permitted to download, forward or distribute the text or part of it, without the consent of the author(s) and/or copyright holder(s), unless the work is under an open content license such as Creative Commons.

Abstract

Traditionally, Event-Triggered Control (ETC) methods are sample-and-hold control schemes that implement a triggering condition in order to reduce the number of control updates. Given a decay rate of the Lyapunov function, they focus on minimizing the (average) Inter-Sample Time (IST). In this thesis, we focused on the scheduling of Periodic Event-Triggered Control (PETC) controllers. By dynamically switching between triggering conditions, we
are maximizing the average rate of decay of the Common Lyapunov Function (CLF) given a minimum Average Inter Sample Time (AIST) or burst condition.

Given the physical system, we construct a switched system which captures all possible
scheduling behaviors. The l-complete abstraction of the switched system is constructed by solving a conjunction of quadratic equations. By setting a minimum AIST or burst condition, a set of states in the abstraction is marked and a safety game is played to construct the Maximal Permissive Controller (MPC).

On the safe behaviors inside the MPC, the guaranteed minimum control performance is maximized for the infinite horizon problem, i.e. by maximizing the minimum weighted time average of the primitive cycles in the MPC. First, several energy games are played to estimate
the maximum control performance. Thereafter, a mean-payoff game is played to generate the strategy securing this maximum control performance, which is used to construct the infinite horizon controller.

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