交叉口(航空)
模型预测控制
动态规划
数学优化
运动规划
控制理论(社会学)
事件(粒子物理)
时间范围
计算机科学
无人地面车辆
功能(生物学)
近似算法
路径(计算)
工程类
数学
控制(管理)
人工智能
航空航天工程
进化生物学
量子力学
生物
机器人
物理
程序设计语言
作者
Chaofang Hu,Lingxue Zhao,Ge Qu
出处
期刊:IEEE Transactions on Vehicular Technology
[Institute of Electrical and Electronics Engineers]
日期:2021-11-01
卷期号:70 (11): 11228-11243
被引量:21
标识
DOI:10.1109/tvt.2021.3111692
摘要
Autonomous driving of unmanned ground vehicle (UGV) at road intersection is a challenging task due to the complicated traffic conditions. In this paper, an event-triggered model predictive adaptive dynamic programming (MPADP) algorithm is proposed for path planning of UGV at road intersection. Following the critic-actor scheme of adaptive dynamic programming (ADP), cost function approximation and control policy generation are combined to formulate MPADP. The infinite horizon cost function of ADP is stacked over predictive horizon of model predictive control (MPC), and then the infinite horizon cost function is converted to the finite horizon-stacked cost function in MPADP. By minimizing the approximation error within predictive horizon, the approximation accuracy is enhanced. Considering the limitation of energy consumption, the event-triggered mechanism is designed based on the mismatch of cost function approximation. Three triggering conditions are designed, and the corresponding boundedness of approximation error is proved. Simulation results illustrate the effectiveness, efficiency and feasibility in application of the event-triggered MPADP method for path planning at road intersection.
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