This paper aims to propose a new computational guidance method for missiles that can satisfy multiple practical constraints, such as impact angle and time, seeker’s field-of-view, and acceleration constraints. The proposed method is based on the model predictive path integral (MPPI) with a novel adaptive weight scheme. MPPI control is a numerical optimization approach that solves optimal control problems using a stochastic process. In this approach, the optimal control inputs are repeatedly updated to minimize the cost functions of sampled state trajectories generated by propagating the system model with a noise input. However, in the conventional MPPI architecture, the cost functions for constraints are typically formulated using fixed weight values, and it is also challenging to handle terminal constraints. Finding appropriate weight values for each cost function and the terminal constraints requires a lot of effort, making the conventional MPPI approach inadequate for solving optimal control problems with multiple constraints and terminal constraints, such as multiconstrained guidance problems. To solve this problem, we propose a new method called adaptive weight MPPI control. The cost weights are automatically adjusted using the estimated states from the sampled state trajectories. This proposed MPPI architecture allows us to solve multiconstrained guidance problems without carefully tuning the weight values. Moreover, the proposed method can be easily applied to various multiconstrained guidance problems without significant configuration changes in guidance algorithms. Numerical simulations are performed for various engagement conditions to verify the effectiveness and feasibility of the proposed guidance algorithm in this study.


    Access

    Check access

    Check availability in my library

    Order at Subito €


    Export, share and cite



    Title :

    Adaptive Weight Model Predictive Path Integral Control for Multiconstrained Missile Guidance


    Contributors:


    Publication date :

    2024-01-31


    Size :

    18 pages




    Type of media :

    Article (Journal)


    Type of material :

    Electronic Resource


    Language :

    English




    Multiconstrained Real-Time Entry Guidance Using Deep Neural Networks

    Cheng, Lin / Jiang, Fanghua / Wang, Zhenbo et al. | IEEE | 2021


    Path-Following Guidance Using Model Predictive Path Integral Control

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



    Intelligent Online Multiconstrained Reentry Guidance Based on Hindsight Experience Replay

    Qingji Jiang / Xiaogang Wang / Yuliang Bai et al. | DOAJ | 2023

    Free access

    Predictive Missile Guidance

    R. A. Best / J. P. Norton | AIAA | 2000