Dynamic Modelling and Optimal Control of Autonomous Heavy-duty Vehicles
Abstract: Autonomous vehicles have gained much importance over the last decade owing to their promising capabilities like improvement in overall traffic flow, reduction in pollution and elimination of human errors. However, when it comes to long-distance transportation or working in complex isolated environments like mines, various factors such as safety, fuel efficiency, transportation cost, robustness, and accuracy become very critical. This thesis, developed at the Connected and Autonomous Systems department of Scania AB in association with KTH, focuses on addressing the issues related to fuel efficiency, robustness and accuracy of an autonomous heavy-duty truck used for mining applications. First, in order to improve the state prediction capabilities of the simulation model, a comparative analysis of two dynamic bicycle models was performed. The first model used the empirical PAC2002 Magic Formula (MF) tyre model to generate the tyre forces, and the latter used a piece-wise Linear approximation of the former. On top of that, in order to account for the nonlinearities and time delays in the lateral direction, the steering dynamic equations were empirically derived and cascaded to the vehicle model. The fidelity of these models was tested against real experimental logs, and the best vehicle model was selected by striking a balance between accuracy and computational efficiency. The Dynamic bicycle model with piece-wise Linear approximation of tyre forces proved to tick-all-the-boxes by providing accurate state predictions within the acceptable error range and handling lateral accelerations up to 4 m/s2. Also, this model proved to be six times more computationally efficient than the industry-standard PAC2002 tyre model. Furthermore, in order to ensure smooth and accurate driving, several Model Predictive Control (MPC) formulations were tested on clothoid-based Single Lane Change (SLC), Double Lane Change (DLC) and Truncated Slalom trajectories with added disturbances in the initial position, heading and velocities. A linear time-varying Spatial error MPC is proposed, which provides a link between spatial-domain and time-domain analysis. This proposed controller proved to be a perfect balance between fuel efficiency which was achieved by minimising braking and acceleration sequences and offset-free tracking along with ensuring that the truck reached its destination within the stipulated time irrespective of the added disturbances. Lastly, a comparative analysis between various Prediction-Simulation model pairs was made, and the best pair was selected in terms of its robustness to parameter changes, simplicity, computational efficiency and accuracy.
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