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Model Predictive Control for pathtracking and obstacle avoidance of autonomous vehicle
KTH, School of Electrical Engineering and Computer Science (EECS).
2018 (English)Independent thesis Advanced level (degree of Master (Two Years)), 20 credits / 30 HE creditsStudent thesis
Abstract [en]

The concept of autonomous vehicles has been widelyexplored lately by, among others, automotive companies as away to for example improve fuel efficiency or to gain accessto environments which pose a danger to human operators.Model Predictive Control (MPC) has traditionally been used tocontrol systems with slower dynamics but with the emergence ofmore powerful computers it is now being used in systems withconsiderably faster dynamics as well. One of the main strengthsof MPC is its ability to handle constraints which are present inall physical systems. The aim of this thesis was to develop a singlelayer linear controller for path tracking and obstacle avoidanceof an autonomous car. Its ability to minimize the deviations tothe reference path while clearing static obstacles was evaluated.Focus was placed on the tracking problem hence no trajectoryplanning system was implemented. Instead a predefined pathwas used. Simulations were developed in MATLAB based on thekinematic bicycle model. The performance of the controller wasfurther tested at Smart Mobility Lab (SML) in KTH where amodified R/C car was controlled through Robotics OperatingSystem (ROS). The results from the experiments showed that itwas able to successfully evade the obstacles while tracking thepath. However, in the experiments the vehicle failed to respectthe requirements on maximum deviation from the obstacles andthe path.

Abstract [sv]

Konceptet autonoma fordon har påkat av blandannat bilindustrin som ett sätt att exempelvis förbättra bränsleekonomi eller få tillgång till miljöer som utgör en fara för mänskliga operatörer. Modell-prediktiv reglering (MPC) har traditionellt använts för att styra system med långsam dynamik men i och med uppkomsten av kraftfullare datorer används det nu i system med avsevärt snabbare dynamik. En av de huvudsakliga styrkorna hos MPC är dess förmåga att hantera restriktioner som finns i alla fysikaliska system. Målet med den här uppsatsen var att utveckla en linjär regulator bestående av ett lager för vägföljning och undvikning av hinder för en autonom bil. Dess förmåga att minimera avvikelser till referensbanan och samtidigt undvika statiska hinder utvärderades. Fokus placerades på spårningen av referensbanan följaktligen implementeras inte något system för planering av banan. Istället användes en fördefinierad bana. Simuleringar utvecklades i matlab baserat på den kinematiska cykelmodellen. Regulatorns prestanda utvärderas vidare på Smart Mobility Lab (SML) på KTH där en modifierad radiostyrd bil styrdes via Robotics Operting System (ROS). Resultaten från experimenten visade på att bilen klarar av att undvika hindren samtidigt som den följde banan. Dock misslyckades bilen i experimenten att respektera kraven på maximal avvikelsefrån hindren och banan.

Place, publisher, year, edition, pages
2018. , p. 10
Series
TRITA-EECS-EX ; 2018:654
National Category
Engineering and Technology
Identifiers
URN: urn:nbn:se:kth:diva-239888OAI: oai:DiVA.org:kth-239888DiVA, id: diva2:1268012
Educational program
Master of Science - Aerospace Engineering
Supervisors
Examiners
Available from: 2018-12-04 Created: 2018-12-04 Last updated: 2018-12-04Bibliographically approved

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