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Distributed Model Predictive Control for Rendezvous Problem
KTH, School of Electrical Engineering and Computer Science (EECS).
2019 (English)Independent thesis Advanced level (degree of Master (Two Years)), 20 credits / 30 HE creditsStudent thesis
Abstract [en]

This thesis investigates the potential advantages and disadvantages of using adistributed control approach to land an autonomous drone on an autonomousboat. The expected advantages include better utilisation of computational resources,as well as increased robustness towards communication delays. Inthis context, distributed control means that separate computers on the droneand boat are both involved in computing the control inputs to the system. Thisstands in contrast to an existing centralised algorithm where all computationsfor finding the control input are performed on the drone. Two new algorithmsare proposed, one using distributed model predictive control (DMPC) and oneusing a combination of DMPC with linear state-space feedback. The followingproperties of all the algorithms are tested: what the longest possible predictionhorizon with sufficiently short solution time is, how long it takes to solve optimisationproblems for the algorithms, and how quickly and safely each algorithmcan land the drone. Finally, the DMPC algorithm is shown to in certainscenarios possess improved robustness towards communication delays.

Abstract [sv]

Den här masteruppsatsen undersöker de potentiella för- och nackdelarna medatt använda distribuerad reglering för att landa en autonom drönare på en autonombåt. De förväntade fördelarna inkluderar bättre användning av beräkningsresursersamt ökad robusthet mot fördröjningar i kommunikation mellanfordonen. Här betyder distribuerad reglering att separata datorer beräknar delarav systemets styrsignal. Detta skiljer sig från en redan existerande centraliseradlösning där drönaren själv beräknar alla styrsignaler. Två nya algoritmerföreslås, en som använder sig utav distribuerad modell-prediktiv reglering(DMPC) och en som använder sig av en kombination av DMPC och linjärtillstånds-återkoppling. De följande egenskaperna av algoritmerna testas:vilken den längsta möjliga prediktionshorisonten med tillräckligt snabb iterationstidvar, hur lång tid det tar att lösa optimeringsproblem för varje algoritmoch hur snabbt och säkert varje algoritm kunde landa drönaren. Slutligen så visadedet sig att i vissa scenarion så har DMPC-algoritmen förbättrad robusthetmot kommunikationsproblem.

Place, publisher, year, edition, pages
2019. , p. 56
Series
TRITA-EECS-EX ; 2019:418
National Category
Engineering and Technology
Identifiers
URN: urn:nbn:se:kth:diva-254424OAI: oai:DiVA.org:kth-254424DiVA, id: diva2:1332107
Educational program
Master of Science - Systems, Control and Robotics
Supervisors
Examiners
Available from: 2019-06-27 Created: 2019-06-27 Last updated: 2019-06-27Bibliographically approved

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