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Control strategies evaluation for stabilizing a quadcopter docking on a side wall.
KTH, School of Industrial Engineering and Management (ITM).
2019 (English)Independent thesis Advanced level (degree of Master (Two Years)), 20 credits / 30 HE creditsStudent thesisAlternative title
Kontrollstrategier utvärdering för att stabilisera en quadcopter dockning på en sidovägg. (Swedish)
Abstract [en]

This thesis contemplates the stabilization of a quadcopter in physical contact with a side wall via a mechanical arm. The drone is accelerated aiming to apply a greater force against the wall. Consequently, the drone adopts a tilted position that must be controlled. Three control strategies; Proportional-integral-derivative (PID) control, sliding mode control (SMC) and model predictive control (MPC) together with two different arm designs are iterated to achieve the fastest and stable approach. Firstly, a derivation of the drone dynamics for each arm is achieved. Secondly, the different control strategies are implemented based on the derived dynamics. The obtained results show that the drone is capable of docking at a certain speed, remaining stable while exerting a force against the wall.

Abstract [sv]

Denna avhandling överväger stabilisering av en quadcopter i fysisk kontakt med en sidovägg genom en mekanisk arm. Dronen accelereras med sikte på att tillämpa en större kraft mot väggen. Följaktligen adopterar dronen ett lutat läge som måste styras. Tre kontrollstrategier; Proportional-integral-derivat (PID) kontroll, glidstyrningskontroll (SMC) och modell prediktiv kontroll (MPC) tillsammans med två olika arm design är iterated till uppnå det snabbaste och stabila tillvägagångssättet. För det första en avledning av drondynamiken för varje arm är uppnådd. För det andra implementeras de olika kontrollstrategierna baserat på det härledda dynamik. De erhållna resultaten visar att dronen är kapabel att docka i en viss hastighet, Återstående stabila samtidigt som man utövar en kraft mot väggen.

Place, publisher, year, edition, pages
2019. , p. 84
Series
TRITA-EECS-EX ; 2019:377
National Category
Engineering and Technology
Identifiers
URN: urn:nbn:se:kth:diva-263097OAI: oai:DiVA.org:kth-263097DiVA, id: diva2:1366443
Supervisors
Examiners
Available from: 2019-10-30 Created: 2019-10-29 Last updated: 2019-10-30Bibliographically approved

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Citation style
  • apa
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Output format
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