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Riderless self-balancing bicycle: Derivation and implementation of a time variant linearized state space model for balancing a bicycle in motion by turning the front wheel
KTH, School of Industrial Engineering and Management (ITM), Machine Design (Dept.), Mechatronics.
KTH, School of Industrial Engineering and Management (ITM), Machine Design (Dept.), Mechatronics.
2018 (English)Independent thesis Basic level (degree of Bachelor), 10 credits / 15 HE creditsStudent thesis
Abstract [en]

Self-driving vehicles are becoming more and more prevalent in society, with buses and cars close to being implemented in the public domain. Self-driving two-wheeled vehicles could be a solution for space-efficient transportation in cities, where space is becoming a larger issue.

The purpose of this project was to develop and implement a linearized time variant state space model for balancing such a two-wheeled vehicle in the form of a bicycle by turning its front wheel. To test the derived model a small demonstrator was built and experimented with.

The final conclusion was that the model could be a simple solution for balancing an electric bicycle. However, further experimentation on a bigger scale would have to be done to reach a more decisive conclusion.

Abstract [sv]

Självkörande fordon börjar bli en allt större verklighet i samhället, där bussar och bilar snart kan komma att implementeras på större skala. Självkörande tvåhjuliga fordon kan vara en möjlig lösning på mindre fordon i städer där utrymme blir mer och mer sparsamt.

Syftet med detta projekt har varit att ta fram och implementera en linjäriserad tidsvarierande tillståndsmodell för balansreglering av en elcykel genom vridning av framhjulet. För att testa modellen konstruerades en liten demonstrator med vilken experiment och tester utfördes.

Den slutsats som drogs var att modellen mycket väl skulle kunna vara en lösning för balansering av en elcykel, men att fortsatta undersökningar bör genomföras på en större skala för att en mer definitiv slutsats skall kunna dras.

Place, publisher, year, edition, pages
2018. , p. 84
Series
TRITA-ITM-EX 2018 ; 58
National Category
Mechanical Engineering
Identifiers
URN: urn:nbn:se:kth:diva-232995OAI: oai:DiVA.org:kth-232995DiVA, id: diva2:1237256
Supervisors
Examiners
Available from: 2018-08-08 Created: 2018-08-08 Last updated: 2018-08-08Bibliographically approved

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CiteExportLink to record
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Citation style
  • apa
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Output format
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