In this paper, a model predictive path integral control algorithm based on a generalized importance sampling scheme is developed and parallel optimization via sampling is performed using a graphics processing unit. The proposed generalized importance sampling scheme allows for changes in the drift and diffusion terms of stochastic diffusion processes and plays a significant role in the performance of the model predictive control algorithm. The proposed algorithm is compared in simulation with a model predictive control version of differential dynamic programming on nonlinear systems. Finally, the proposed algorithm is applied on multiple vehicles for the task of navigating through a cluttered environment. The current simulations illustrate the efficiency and robustness of the proposed approach and demonstrate the advantages of computational frameworks that incorporate concepts from statistical physics, control theory, and parallelization against more traditional approaches of optimal control theory.


    Access

    Check access

    Check availability in my library

    Order at Subito €


    Export, share and cite



    Title :

    Model Predictive Path Integral Control: From Theory to Parallel Computation


    Contributors:

    Published in:

    Publication date :

    2017-01-23


    Size :

    14 pages




    Type of media :

    Article (Journal)


    Type of material :

    Electronic Resource


    Language :

    English





    Path-Following Guidance Using Model Predictive Path Integral Control

    Jeong, Eui-Taek / Lee, Chang-Hun | Springer Verlag | 2022


    FLYING COMPLEX MANEUVERS WITH MODEL PREDICTIVE PATH INTEGRAL CONTROL

    Pravitra, Jintasit / Theodorou, Evangelos / Johnson, Eric N. | TIBKAT | 2021


    Flying Complex Maneuvers with Model Predictive Path Integral Control

    Pravitra, Jintasit / Theodorou, Evangelos / Johnson, Eric N. | AIAA | 2021