Particle Filtering and Optimal Control for Vehicles and Robots
Författare
Summary, in English
A second topic is sensor fusion for improved autonomy in vehicles. A novel approach to model-based joint wheel-slip and motion estimation of four-wheeled vehicles is developed. Unlike other approaches, the method explicitly models the nonlinear slip dynamics in the state and measurement equations. Excellent and consistent accuracy for all relevant states are reported, both during steady-state driving and aggressive maneuvering. The method applies to general classes of four-wheeled vehicles and it only assumes kinematic relationships.
Optimization-based control methods have found their way into automotive applications.
Optimal control for vehicles typically results in control inputs that give aggressive maneuvering. Proper models are therefore crucial. An investigation on what impact different vehicle models and road surfaces have on the optimal trajectories in safety-critical maneuvers is presented. One conclusion is that the control-input behavior is highly sensitive to the choice of chassis and tire models. Another conclusion is that the optimal
driving techniques are different depending on tire-road characteristics.
The conclusions motivate the design of a novel, two-level hierarchical approach to optimal trajectory generation for wheeled vehicles. The first novelty is the use of a nonlinear vehicle model with tire modeling in the optimization problem at the high level. This provides for better coupling with the low-level controller, which uses a nonlinear model predictive controller (MPC) for allocating the torques and steer angles to the wheels. This is combined with a linear MPC, which is used when the nonlinear MPC fails to converge in time.
The thesis also describes a hierarchical design flow for performing online, minimum-time trajectory generation for four-wheeled vehicles with independent steer
and drive actuation, combined with real-time obstacle avoidance. The approach is based on convex optimization. It therefore allows fast
computations, both for trajectory generation and online feedback-based obstacle avoidance. The proposed method is fully implemented on a
pseudo-omnidirectional mobile platform and evaluated in experiments in
a path-tracking scenario.
Avdelning/ar
Publiceringsår
2014
Språk
Engelska
Publikation/Tidskrift/Serie
PhD Thesis TFRT-1101
Fulltext
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Dokumenttyp
Doktorsavhandling
Förlag
Department of Automatic Control, Lund Institute of Technology, Lund University
Ämne
- Control Engineering
Nyckelord
- particle filtering
- optimal control
- automotive systems
- out-of-sequence measurement
- autonomy
- model predictive control
- sensor fusion
- wheel slip
- dynamic optimization
Status
Published
Projekt
- ENGROSS
Forskningsgrupp
- LCCC
- ELLIIT
Handledare
ISBN/ISSN/Övrigt
- ISSN: 0280-5316
- ISSN: 0280-5316
- ISBN: 978-91-7473-948-0
Försvarsdatum
23 maj 2014
Försvarstid
13:15
Försvarsplats
Lecture hall M:B, M-building, Ole Römers väg 1, Lund University Faculty of Engineering
Opponent
- Uwe D. Hanebeck (Prof)