Print Email Facebook Twitter Real-time receding horizon trajectory generation for long heavy vehicle combinations on highways Title Real-time receding horizon trajectory generation for long heavy vehicle combinations on highways Author Van Duijkeren, N.J. Contributor Keviczky, T. (mentor) Laine, L. (mentor) Nilsson, P. (mentor) Faculty Mechanical, Maritime and Materials Engineering Department Delft Center for Systems and Control Programme Systems & Control Date 2014-09-04 Abstract The so-called A-double long heavy vehicle combination is a 32 meter long and up to 80 tonnes heavy double-trailer truck widely used in Canada and Australia today, and will be more abundant on the road also in Europe in the near future. They have the potential to decrease road transport cost, traffic congestion and generate lower emissions than current road freight transport. However, an undesired effect of the added towed units is the increase in difficulty to maneuver truck on roads and in busy traffic. The increasing complexity for truck drivers to handle trivial tasks like lane changing call for advanced driver assistance functions. The development of advanced assistance systems or potentially autonomous functioning trucks can improve traffic safety, allowing for further increase in use of long combination trucks. This thesis work focuses on one crucial element of such driver assistance systems, the ability to plan a safe and smooth trajectory and generate control signals for the low level actuators. Based on the measured vehicle-state, the road curvature and measurements of surrounding vehicles, a (sub)optimal steering action and cruise control reference velocity ought to be generated. A receding horizon optimal control problem (OCP) is formulated, with a nonlinear single-track vehicle prediction model for the A-double combination. The OCP is designed to capture the main highway driving tasks of lane keeping, lane changing and collision avoidance. A direct multiple-shooting solution strategy to this OCP is implemented using the automatic code-generation functionality of the ACADO toolkit. Results of closed-loop simulations are presented for the control of both the vehicle prediction model and a high-fidelity vehicle model, developed and validated by Volvo Group Truck Technology. Because of the computational performance advantages of the Real-Time Iteration algorithm and the automatic code-generation for the solution scheme, real-time performance is achieved for the optimization-based receding horizon trajectory generator for the A-double combination. Subject model predictive controlmpcreceding horizonrhcreal-timetrajectory generationtrajectory generatorlong combinationtruckdriver assistancecollision avoidancehighwayvolvononlinear programmingoptimal controlA-double To reference this document use: http://resolver.tudelft.nl/uuid:5434052d-b939-4544-92df-ab584b5367d7 Part of collection Student theses Document type master thesis Rights (c) 2014 Van Duijkeren, N.J. Files PDF nvanduijkeren_final_msc.pdf 11.11 MB Close viewer /islandora/object/uuid:5434052d-b939-4544-92df-ab584b5367d7/datastream/OBJ/view