Predictive control for autonomous driving: With experimental evaluation on a heavy-duty construction truck
2016 (English)Licentiate thesis, monograph (Other academic)
Autonomous vehicles is a rapidly expanding field, and promise to play an important role in society. In more isolated environments, vehicle automation can bring significant efficiency and production benefits and it eliminates repetitive jobs that can lead to inattention and accidents.
The thesis addresses the problem of lateral and longitudinal dynamics control of autonomous ground vehicles with the purpose of accurate and smooth path following. Clothoids are used in the design of optimal predictive controllers aimed at minimizing the lateral forces and jerks in the vehicle.
First, a clothoid-based path sparsification algorithm is proposed to efficiently describe the reference path. This approach relies on a sparseness regularization technique such that a minimal number of clothoids is used to describe the reference path.
Second, a clothoid-based model predictive controller (MPCC) is proposed. This controller aims at producing a smooth driving by taking advantage of the clothoid properties.
Third, we formulate the problem as an economic model predictive controller (EMPC). In EMPC the objective function contains an economic cost (here represented by comfort or smoothness), which is described by the second and first derivatives of the curvature.
Fourth, the generation of feasible speed profiles, and the longitudinal vehicle control for following these, is studied. The speed profile generation is formulated as an optimization problem with two contradictory objectives: to drive as fast as possible while accelerating as little as possible. The longitudinal controller is formulated in a similar way, but in a receding horizon fashion.
The experimental evaluation with the EMPC demonstrates its good performance, since the deviation from the path never exceeds 30 cm and in average is 6 cm. In simulation, the EMPC and the MPCC are compared with a pure-pursuit controller (PPC) and a standard MPC. The EMPC clearly outperforms the PPC in terms of path accuracy and the standard MPC in terms of driving smoothness.
Place, publisher, year, edition, pages
Universitetsservice US AB: KTH Royal Institute of Technology, 2016. , x, 126 p.
TRITA-EE, ISSN 1653-5146
Autonomous vehicles, model predictive control, automatic control, heavy-duty vehicles, mining, trucks, clothoids
Research subject Electrical Engineering
IdentifiersURN: urn:nbn:se:kth:diva-186123ISBN: 978-91-7595-984-9OAI: oai:DiVA.org:kth-186123DiVA: diva2:925562
2016-05-23, Q2, Osquldas väg 10, Q-huset, våningsplan 2, KTH Campus, Stockholm, 14:00 (English)
Bemporad, Alberto, Professor
Wahlberg, Bo, ProfessorMårtensson, Jonas, Assistant professor
QC 201605032016-05-032016-05-022016-05-03Bibliographically approved