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Ljungqvist, O. (2019). On motion planning and control for truck and trailer systems. (Licentiate dissertation). Linköping: Linköping University Electronic Press
Open this publication in new window or tab >>On motion planning and control for truck and trailer systems
2019 (English)Licentiate thesis, comprehensive summary (Other academic)
Abstract [en]

During the last decades, improved sensor and hardware technologies as well as new methods and algorithms have made self-driving vehicles a realistic possibility in the near future. Thanks to this technology enhancement, many leading automotive and technology companies have turned their attention towards developing advanced driver assistance systems (ADAS) and self-driving vehicles. Autonomous vehicles are expected to have their first big impact in closed areas, such as mines, harbors and loading/offloading sites. In such areas, the legal requirements are less restrictive and the surrounding environment is more controlled and predictable compared to urban areas. Expected positive outcomes include increased productivity and safety, reduced emissions and the possibility to relieve the human from performing complex or dangerous tasks. Within these sites, different truck and trailer systems are used to transport materials. These systems are composed of several interconnected modules, and are thus large and highly unstable while reversing. This thesis addresses the problem of designing efficient motion planning and feedback control frameworks for such systems.

First, a cascade controller for a reversing truck with a dolly-steered trailer is presented. The unstable modes of the system is stabilized around circular equilibrium configurations using a gain-scheduled linear quadratic (LQ) controller together with a higher-level pure pursuit controller to enable path following of piecewise linear reference paths. The cascade controller is then used within a rapidly-exploring random tree (RRT) framework and the complete motion planning and control framework is demonstrated on a small-scale test vehicle.

Second, a path following controller for a reversing truck with a dolly-steered trailer is proposed for the case when the obtained motion plan is kinematically feasible. The control errors of the system are modeled in terms of their deviation from the nominal path and a stabilizing LQ controller with feedforward action is designed based on the linearization of the control error model. Stability of the closed-loop system is proven by combining global optimization, theory from linear differential inclusions and linear matrix inequality techniques.

Third, a systematic framework is presented for analyzing stability of the closed-loop system consisting of a controlled vehicle and a feedback controller, executing a motion plan computed by a lattice planner. When this motion planner is considered, it is shown that the closed-loop system can be modeled as a nonlinear hybrid system. Based on this, a novel method is presented for analyzing the behavior of the tracking error, how to design the feedback controller and how to potentially impose constraints on the motion planner in order to guarantee that the tracking error is bounded and decays towards zero.

Fourth, a complete motion planning and control solution for a truck with a dolly-steered trailer is presented. A lattice-based motion planner is proposed, where a novel parametrization of the vehicle’s state-space is proposed to improve online planning time. A time-symmetry result is established that enhance the numerical stability of the numerical optimal control solver used for generating the motion primitives. Moreover, a nonlinear observer for state estimation is developed which only utilizes information from sensors that are mounted on the truck, making the system independent of additional trailer sensors. The proposed framework is implemented on a full-scale truck with a dolly-steered trailer and results from a series of field experiments are presented.

Place, publisher, year, edition, pages
Linköping: Linköping University Electronic Press, 2019. p. 78
Series
Linköping Studies in Science and Technology. Licentiate Thesis, ISSN 0280-7971 ; 1832
National Category
Control Engineering Vehicle Engineering Robotics Embedded Systems Computer Engineering
Identifiers
urn:nbn:se:liu:diva-153892 (URN)10.3384/lic-diva-153892 (DOI)9789176851302 (ISBN)
Presentation
2019-01-25, Ada Lovelace, B-building, Campus Valla, 10:15 (English)
Opponent
Supervisors
Available from: 2019-01-17 Created: 2019-01-17 Last updated: 2019-01-22Bibliographically approved
Ling, G., Lindsten, K., Ljungqvist, O., Löfberg, J., Norén, C. & Larsson, C. A. (2018). Fuel-efficient Model Predictive Control for Heavy Duty Vehicle Platooning using Neural Networks. In: 2018 American Control Conference (ACC): . Paper presented at 2018 American Control Conference (ACC) (pp. 3994-4001). IEEE
Open this publication in new window or tab >>Fuel-efficient Model Predictive Control for Heavy Duty Vehicle Platooning using Neural Networks
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2018 (English)In: 2018 American Control Conference (ACC), IEEE, 2018, p. 3994-4001Conference paper, Published paper (Refereed)
Abstract [en]

The demand for fuel-efficient transport solutions are steadily increasing with the goal of reducing environmental impact and increasing efficiency. Heavy-Duty Vehicle (HDV) platooning is a promising concept where multiple HDVs drive together in a convoy with small intervehicular spacing. By doing this, the aerodynamic drag is reduced which in turn lowers fuel consumption. We propose a novel Model Predictive Control (MPC) framework for longitudinal control of the follower vehicle in a platoon consisting of two HDVs when no vehicle-to-vehicle communication is available. In the framework, the preceding vehicle's velocity profile is predicted using artificial neural networks which uses a topographic map of the road as input and is trained offline using synthetic data. The gear shifting and mass of consumed fuel for the controlled follower vehicle is modeled and used within the MPC controller. The efficiency of the proposed framework is verified in simulation examples and is benchmarked with a currently available control solution.  

Place, publisher, year, edition, pages
IEEE, 2018
Series
American Control Conference (ACC), E-ISSN 2378-5861
National Category
Control Engineering Robotics
Identifiers
urn:nbn:se:liu:diva-152456 (URN)10.23919/ACC.2018.8431520 (DOI)978-1-5386-5428-6 (ISBN)978-1-5386-5427-9 (ISBN)978-1-5386-5429-3 (ISBN)
Conference
2018 American Control Conference (ACC)
Available from: 2018-11-01 Created: 2018-11-01 Last updated: 2018-11-30
Ljungqvist, O., Axehill, D. & Löfberg, J. (2018). On stability for state-lattice trajectory tracking control. In: 2018 Annual American Control Conference (ACC): . Paper presented at 2018 Annual American Control Conference (ACC) June 27–29, 2018. Wisconsin Center, Milwaukee, USA (pp. 5868-5875). IEEE
Open this publication in new window or tab >>On stability for state-lattice trajectory tracking control
2018 (English)In: 2018 Annual American Control Conference (ACC), IEEE, 2018, p. 5868-5875Conference paper, Published paper (Refereed)
Abstract [en]

In order to guarantee that a self-driving vehicle is behaving as expected, stability of the closed-loop system needs to be rigorously analyzed. The key components for the lowest levels of control in self-driving vehicles are the controlled vehicle, the low-level controller and the local planner.The local planner that is considered in this work constructs a feasible trajectory by combining a finite number of precomputed motions. When this local planner is considered, we show that the closed-loop system can be modeled as a nonlinear hybrid system. Based on this, we propose a novel method for analyzing the behavior of the tracking error, how to design the low-level controller and how to potentially impose constraints on the local planner, in order to guarantee that the tracking error is bounded and decays towards zero. The proposed method is applied on a truck and trailer system and the results are illustrated in two simulation examples.

Place, publisher, year, edition, pages
IEEE, 2018
Series
American Control Conference (ACC), E-ISSN 2378-5861
National Category
Control Engineering Robotics
Identifiers
urn:nbn:se:liu:diva-152455 (URN)10.23919/ACC.2018.8430822 (DOI)978-1-5386-5428-6 (ISBN)978-1-5386-5427-9 (ISBN)978-1-5386-5429-3 (ISBN)
Conference
2018 Annual American Control Conference (ACC) June 27–29, 2018. Wisconsin Center, Milwaukee, USA
Available from: 2018-11-01 Created: 2018-11-01 Last updated: 2019-01-17
Andersson, O., Ljungqvist, O., Tiger, M., Axehill, D. & Heintz, F. (2018). Receding-Horizon Lattice-based Motion Planning with Dynamic Obstacle Avoidance. In: 2018 IEEE Conference on Decision and Control (CDC): . Paper presented at 2018 IEEE 57th Annual Conference on Decision and Control (CDC),17-19 December, Miami, Florida, USA (pp. 4467-4474). Institute of Electrical and Electronics Engineers (IEEE)
Open this publication in new window or tab >>Receding-Horizon Lattice-based Motion Planning with Dynamic Obstacle Avoidance
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2018 (English)In: 2018 IEEE Conference on Decision and Control (CDC), Institute of Electrical and Electronics Engineers (IEEE), 2018, p. 4467-4474Conference paper, Published paper (Refereed)
Abstract [en]

A key requirement of autonomous vehicles is the capability to safely navigate in their environment. However, outside of controlled environments, safe navigation is a very difficult problem. In particular, the real-world often contains both complex 3D structure, and dynamic obstacles such as people or other vehicles. Dynamic obstacles are particularly challenging, as a principled solution requires planning trajectories with regard to both vehicle dynamics, and the motion of the obstacles. Additionally, the real-time requirements imposed by obstacle motion, coupled with real-world computational limitations, make classical optimality and completeness guarantees difficult to satisfy. We present a unified optimization-based motion planning and control solution, that can navigate in the presence of both static and dynamic obstacles. By combining optimal and receding-horizon control, with temporal multi-resolution lattices, we can precompute optimal motion primitives, and allow real-time planning of physically-feasible trajectories in complex environments with dynamic obstacles. We demonstrate the framework by solving difficult indoor 3D quadcopter navigation scenarios, where it is necessary to plan in time. Including waiting on, and taking detours around, the motions of other people and quadcopters.

Place, publisher, year, edition, pages
Institute of Electrical and Electronics Engineers (IEEE), 2018
Series
Conference on Decision and Control (CDC), ISSN 2576-2370 ; 2018
Keywords
Motion Planning, Optimal Control, Autonomous System, UAV, Dynamic Obstacle Avoidance, Robotics
National Category
Control Engineering
Identifiers
urn:nbn:se:liu:diva-152131 (URN)10.1109/CDC.2018.8618964 (DOI)9781538613955 (ISBN)9781538613948 (ISBN)9781538613962 (ISBN)
Conference
2018 IEEE 57th Annual Conference on Decision and Control (CDC),17-19 December, Miami, Florida, USA
Funder
VINNOVAKnut and Alice Wallenberg FoundationSwedish Foundation for Strategic Research ELLIIT - The Linköping‐Lund Initiative on IT and Mobile CommunicationsSwedish Research CouncilLinnaeus research environment CADICSCUGS (National Graduate School in Computer Science)
Note

This work was partially supported by FFI/VINNOVA, the Wallenberg Artificial Intelligence, Autonomous Systems and Software Program (WASP) funded by Knut and Alice Wallenberg Foundation, the Swedish Foundation for Strategic Research (SSF) project Symbicloud, the ELLIIT Excellence Center at Linköping-Lund for Information Technology, Swedish Research Council (VR) Linnaeus Center CADICS, and the National Graduate School in Computer Science, Sweden (CUGS).

Available from: 2018-10-18 Created: 2018-10-18 Last updated: 2019-01-30Bibliographically approved
Evestedt, N., Ljungqvist, O. & Axehill, D. (2016). Path tracking and stabilization for a reversing general 2-trailer configuration using a cascaded control approach. In: Intelligent Vehicles Symposium (IV), 2016 IEEE: . Paper presented at 2016 IEEE Intelligent Vehicles Symposium, Gothenburg, Sweden, June 19-22, 2016 (pp. 1156-1161). Institute of Electrical and Electronics Engineers (IEEE)
Open this publication in new window or tab >>Path tracking and stabilization for a reversing general 2-trailer configuration using a cascaded control approach
2016 (English)In: Intelligent Vehicles Symposium (IV), 2016 IEEE, Institute of Electrical and Electronics Engineers (IEEE), 2016, p. 1156-1161Conference paper, Published paper (Refereed)
Abstract [en]

In this paper a cascaded approach for stabilizationand path tracking of a general 2-trailer vehicle configurationwith an off-axle hitching is presented. A low level LinearQuadratic controller is used for stabilization of the internalangles while a pure pursuit path tracking controller is used ona higher level to handle the path tracking. Piecewise linearityis the only requirement on the control reference which makesthe design of reference paths very general. A Graphical UserInterface is designed to make it easy for a user to design controlreferences for complex manoeuvres given some representationof the surroundings. The approach is demonstrated with challengingpath following scenarios both in simulation and on asmall scale test platform.

Place, publisher, year, edition, pages
Institute of Electrical and Electronics Engineers (IEEE), 2016
Keywords
cascade control, control system synthesis, graphical user interfaces, linear quadratic control, mobile robot, path planning, piecewise linear techniques
National Category
Control Engineering
Identifiers
urn:nbn:se:liu:diva-130950 (URN)10.1109/IVS.2016.7535535 (DOI)000390845600183 ()978-1-5090-1821-5 (ISBN)978-1-5090-1822-2 (ISBN)
Conference
2016 IEEE Intelligent Vehicles Symposium, Gothenburg, Sweden, June 19-22, 2016
Projects
iQMatic
Funder
VINNOVA
Available from: 2016-09-01 Created: 2016-09-01 Last updated: 2019-01-17Bibliographically approved
Organisations
Identifiers
ORCID iD: ORCID iD iconorcid.org/0000-0002-1795-5992

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