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Kinodynamic Motion Planning via Funnel Control for Underactuated Unmanned Surface Vehicles
KTH, School of Electrical Engineering and Computer Science (EECS), Intelligent systems, Decision and Control Systems (Automatic Control).ORCID iD: 0000-0002-9706-8073
Department of Electrical Engineering, Division of Signals and Systems, Uppsala University, Uppsala, Sweden.ORCID iD: 0000-0002-4289-2866
KTH, School of Electrical Engineering and Computer Science (EECS), Intelligent systems, Decision and Control Systems (Automatic Control).ORCID iD: 0000-0001-7309-8086
KTH, School of Electrical Engineering and Computer Science (EECS), Intelligent systems, Decision and Control Systems (Automatic Control).ORCID iD: 0000-0002-1927-1690
2024 (English)In: IEEE Transactions on Control Systems Technology, ISSN 1063-6536, E-ISSN 1558-0865, p. 1-12Article in journal (Refereed) Epub ahead of print
Abstract [en]

We develop an algorithm to control an underactuated unmanned surface vehicle (USV) using kinodynamic motion planning with funnel control (KDF). KDF has two key components: motion planning used to generate trajectories with respect to kinodynamic constraints, and funnel control, also referred to as prescribed performance control (PPC), which enables trajectory tracking in the presence of uncertain dynamics and disturbances. We extend PPC to address the challenges posed by underactuation and control input saturation present on the USV. The proposed scheme guarantees stability under user-defined prescribed performance functions where model parameters and exogenous disturbances are unknown. Furthermore, we present an optimization problem to obtain smooth, collision-free trajectories while respecting kinodynamic constraints. We deploy the algorithm on a USV and verify its efficiency in real-world open-water experiments.

Place, publisher, year, edition, pages
2024. p. 1-12
National Category
Control Engineering
Research subject
Electrical Engineering
Identifiers
URN: urn:nbn:se:kth:diva-352180DOI: 10.1109/tcst.2024.3396027ISI: 001218626900001Scopus ID: 2-s2.0-85192732380OAI: oai:DiVA.org:kth-352180DiVA, id: diva2:1891985
Funder
Swedish Research CouncilKnut and Alice Wallenberg FoundationWallenberg AI, Autonomous Systems and Software Program (WASP)
Note

QC 20240906

Available from: 2024-08-23 Created: 2024-08-23 Last updated: 2024-09-06Bibliographically approved

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Lapandic, DzenanVerginis, ChristosDimarogonas, Dimos V.Wahlberg, Bo
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