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Prescribed performance control guided policy improvement for satisfying signal temporal logic tasks
KTH, School of Electrical Engineering and Computer Science (EECS), Automatic Control.
KTH, School of Electrical Engineering and Computer Science (EECS), Automatic Control.ORCID iD: 0000-0001-7309-8086
2019 (English)In: Proceedings of the American Control Conference, Institute of Electrical and Electronics Engineers (IEEE), 2019, p. 286-291, article id 8814999Conference paper, Published paper (Refereed)
Abstract [en]

Signal temporal logic (STL) provides a user-friendly interface for defining complex tasks for robotic systems. Recent efforts aim at designing control laws or using reinforcement learning methods to find policies which guarantee satisfaction of these tasks. While the former suffer from the trade-off between task specification and computational complexity, the latter encounter difficulties in exploration as the tasks become more complex and challenging to satisfy. This paper proposes to combine the benefits of the two approaches and use an efficient prescribed performance control (PPC) base law to guide exploration within the reinforcement learning algorithm. The potential of the method is demonstrated in a simulated environment through two sample navigational tasks.

Place, publisher, year, edition, pages
Institute of Electrical and Electronics Engineers (IEEE), 2019. p. 286-291, article id 8814999
National Category
Electrical Engineering, Electronic Engineering, Information Engineering
Identifiers
URN: urn:nbn:se:kth:diva-262594Scopus ID: 2-s2.0-85072284215ISBN: 9781538679265 (print)OAI: oai:DiVA.org:kth-262594DiVA, id: diva2:1361918
Conference
2019 American Control Conference, ACC 2019; Philadelphia; United States; 10 July 2019 through 12 July 2019
Note

QC 20191017

Available from: 2019-10-17 Created: 2019-10-17 Last updated: 2019-10-17Bibliographically approved

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Dimarogonas, Dimos V.

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