• Login
    View Item 
    •   eScholar Home
    • Faculty of Engineering & Applied Science
    • Master Theses & Projects
    • View Item
    •   eScholar Home
    • Faculty of Engineering & Applied Science
    • Master Theses & Projects
    • View Item
    JavaScript is disabled for your browser. Some features of this site may not work without it.

    Non-linear model predictive control for autonomous vehicles

    Thumbnail
    View/Open
    Abbas_Muhammad_Awais.pdf (4.097Mb)
    Date
    2011-11-01
    Author
    Abbas, Muhammad Awais
    Metadata
    Show full item record
    Abstract
    With the advent of faster computer processors and better optimization algorithms, Model Predictive Control (MPC) systems are more readily used for real-time applications. This research focuses on the application of MPC to trajectory generation of autonomous vehicles in an online manner. The operating environment is assumed to be unknown with various different types of obstacles. Models of simplified 2-D dynamics of the vehicle are developed, discretized and validated against a nonlinear CarSim vehicle model. The developed model is then used to predict future states of the vehicle. The relationship of the weight transfer to the tire slip angle is investigated. The optimal trajectory tracking problem is formulated in terms of a cost function minimization with constraints. Initially, a gradient descent method is used to minimize the cost function. A MATLAB based MPC controller is developed and interfaced with CarSim in order to test the controller on a vehicle operating in a realistic environment. The effects of varying MPC look-ahead horizon lengths on the computation time, simulation cost and the tracking performance are also investigated. Simulation results show that the new MPC controller provides satisfactory online obstacle avoidance and tracking performance. Also, a trajectory tracking criterion with goal point information is found to be superior to traditional trajectory tracking methods since they avoid causing the vehicle to retreat once a large obstacle is detected on the desired path. It is further demonstrated that at a controller frequency of 20Hz, the implementation is real-time implementable only at shorter horizon lengths.
    URI
    https://hdl.handle.net/10155/206
    Collections
    • Electronic Theses and Dissertations [1323]
    • Master Theses & Projects [418]

    DSpace software copyright © 2002-2016  DuraSpace
    Contact Us | Send Feedback
    Theme by 
    Atmire NV
     

     

    Browse

    All of eScholarCommunities & CollectionsBy Issue DateAuthorsTitlesSubjectsThis CollectionBy Issue DateAuthorsTitlesSubjects

    My Account

    LoginRegister

    DSpace software copyright © 2002-2016  DuraSpace
    Contact Us | Send Feedback
    Theme by 
    Atmire NV