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Repetitive Learning MPC and its application in autonomous race driving


M. Brunner

Master Thesis, SS16

A Learning Model Predictive Control (LMPC) strategy for periodic tasks is presented. The proposed control learns from previous iteration data to improve the performance of a closed loop system in each iteration. A sampled safe set and a terminal cost function are used to guarantee recursive feasibility and non-decreasing performance cost in each iteration. The proposed control is tested on an autonomous racing example. Vehicle dynamics are identified online using Linear Regression. The control strategy is implemented on a 1:10 scale RC race car. Sensor data from an ultrasound-based GPS and IMU are used to estimate the system state. Finally, experiments show the real-time feasibility of optimal trajectory generation with online system identification.

Supervisors: Ugo Rosolia, Prof. Dr. Francesco Borrelli, Prof. Dr. Roy Smith


Type of Publication:

(12)Diploma/Master Thesis

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% Autogenerated BibTeX entry
@PhdThesis { Xxx:2017:IFA_5637
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