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Nonlinear MPC for miniature RC Race Cars


F. Leutwiler

Semester Thesis, FS16 (10507)

In this thesis we investigate how nonlinear MPC can improve the performance in an autonomous racing application. Racing involves driving the car at its handling limits, within the nonlinear region of the tire forces. The standard approach to solve nonlinear MPC in real-time, the so called real-time iteration scheme, where the nonlinear MPC problem is approximated with a linear time-varying MPC problem, does not always result in satisfactory performance, especially regions of strong nonlinearities. To investigate if solving the real nonlinear MPC problem can compensate these shortcomings, we implemented the existing Model Predictive Contouring Controller with FORCES Pro NLP in simulation and experiments. Given the flexibility of the solver, we further investigated the benefits of constraints incorporating the friction circle in our model as well as formulating the obstacle avoidance problem using non-convex constraints. The study showed that, (i) the nonlinear MPC problem, including the obstacle avoidance, can be solved within the required sampling time of tens of milliseconds. (ii) The performance can be increased, e.g., the mean lap time is improved by up to 7% and (iii) in the case of low friction tires the friction circle constraints improve the performance and facilitate the tuning of the controller.

Supervisors: Alexander Liniger, John Lygeros


Type of Publication:

(13)Semester/Bachelor Thesis

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% Autogenerated BibTeX entry
@PhdThesis { Xxx:2016:IFA_5542
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