模型预测控制
力矩(物理)
跳跃式监视
计算机科学
控制理论(社会学)
数学优化
线性化
高斯过程
椭球体
障碍物
避障
残余物
高斯分布
控制(管理)
数学
人工智能
非线性系统
算法
机器人
移动机器人
物理
经典力学
量子力学
天文
政治学
法学
作者
Hanqiu Bao,Qi Kang,Xudong Shi,MengChu Zhou,Haojun Li,Jing An,Khaled Sedraoui
出处
期刊:IEEE transactions on intelligent vehicles
[Institute of Electrical and Electronics Engineers]
日期:2023-04-01
卷期号:8 (4): 2939-2953
被引量:2
标识
DOI:10.1109/tiv.2023.3238023
摘要
Great efforts have been devoted to the intelligent control of autonomous systems. Yet, most of existing methods fail to effectively handle the uncertainty of their environment and models. Uncertain locations of dynamic obstacles pose a major challenge for their optimal control and safety, while their linearization or simplified system models reduce their actual performance. To address them, this paper presents a new model predictive control framework with finite samples and a Gaussian model, resulting in a chance-constrained program. Its nominal model is combined with a Gaussian process. Its residual model uncertainty is learned. The resulting method addresses an efficiently solvable approximate formulation of a stochastic optimal control problem by using approximations for efficient computation. There is no perfect distribution knowledge of a dynamic obstacle's location uncertainty. Only finite samples from sensors or past data are available for moment estimation. We use the uncertainty propagation of a system's state and obstacles’ locations to derive a general collision avoidance condition under tight concentration bounds on the error of the estimated moments. Thus, this condition is suitable for different obstacles, e.g., bounding box and ellipsoid obstacles. We provide proved guarantees on the satisfaction of the chance-constraints corresponding to the nominal yet unknown moments. Simulation examples of a vehicle's control are used to show that the proposed method can well realize autonomous control and obstacle avoidance of a vehicle, when it operates in an uncertain environment with moving obstacles. It outperforms the existing moment methods in both performance and computational time.
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