A Receding Horizon Control Strategy for Autonomous Vehicles in Dynamic Environments

2016 ◽  
Vol 24 (2) ◽  
pp. 695-702 ◽  
Author(s):  
Giuseppe Franze ◽  
Walter Lucia
Keyword(s):  
Control Strategy ◽  
Autonomous Vehicles ◽  
Dynamic Environments ◽  
Receding Horizon Control ◽  
Receding Horizon

scholarly journals Receding horizon control strategy for an electric vehicle with dual-motor coupling system in consideration of stochastic vehicle mass

PLoS ONE ◽  
2018 ◽  
Vol 13 (10) ◽  
pp. e0205212 ◽  
Author(s):  
Hongqiang Guo ◽  
Jinyong Shangguan ◽  
Juan Tang ◽  
Qun Sun ◽  
Hongting Wu
Keyword(s):  
Electric Vehicle ◽  
Control Strategy ◽  
Receding Horizon Control ◽  
Receding Horizon ◽  
Vehicle Mass ◽  
Coupling System

scholarly journals Study of the Impact of Delay on Human Remote Navigators with Application to Receding Horizon Control

2012 ◽  
Vol 3 (2) ◽  
Author(s):  
Chad R. Burns ◽  
Ranxiao F. Wang ◽  
Dušan M. Stipanović
Keyword(s):  
Human Behavior ◽  
Nonlinear Model ◽  
Autonomous Vehicles ◽  
Human Performance ◽  
Receding Horizon Control ◽  
Delay Compensation ◽  
Receding Horizon ◽  
Remote Navigation ◽  
Human In The Loop ◽  
The Impact

AbstractThis paper examines the impact of delays on human performance and human strategies when remotely navigating autonomous vehicles, and develops a robust human inspired delay compensation. Vehicles chosen for the study are ground autonomous vehicles which are allowed to stop, providing an instrumental feature that enables it to capture some important human behavior. The effects of delay on human behavior when remotely navigating autonomous vehicles have been captured by a nonlinear model predictive (also known as receding horizon) controller. This study provides some insights into designing human in-the-loop systems for remote navigation of autonomous vehicles when the delays are not negligible. We offer a human inspired strategy for dealing with delay in a fully autonomous receding horizon controller which we show to be safe and convergent for bounded delays.

scholarly journals Terminal Guidance Law for UAV Based on Receding Horizon Control Strategy

Complexity ◽  
2017 ◽  
Vol 2017 ◽  
pp. 1-19 ◽  
Author(s):  
Zhenglong Wu ◽  
Zhenyu Guan ◽  
Chengwei Yang ◽  
Jie Li
Keyword(s):  
Control Strategy ◽  
Focal Point ◽  
Receding Horizon Control ◽  
Initial Time ◽  
Receding Horizon ◽  
Maneuvering Target ◽  
Guidance Law ◽  
Guidance Information ◽  
Terminal Guidance ◽  
Optimal Type

Terminal guidance law against the maneuvering target is always the focal point. Most of the literatures focus on estimating the acceleration of target and time to go in guidance law, which are difficult to acquire. This paper presents a terminal guidance law based on receding horizon control strategy. The proposed guidance law adopts the basic framework of receding horizon control, and the guidance process is divided into several finite time horizons. Then, optimal control theory and target motion prediction model are used to derive guidance law for minimum time index function with continuous renewal of original conditions at the initial time of each horizon. Finally, guidance law performs repeated iteration until intercepting the target. The guidance law is of subprime optimal type, requiring less guidance information, and does not need to estimate the acceleration of target and time to go. Numerical simulation has verified that the proposed guidance law is more effective than traditional methods on constant and sinusoidal target with bounded acceleration.

scholarly journals Receding Horizon Control Using Graph Search for Multi-Agent Trajectory Planning

2021 ◽  
Author(s):  
Patrick Scheffe ◽  
Matheus Vitor de Andrade Pedrosa ◽  
Kathrin Flaßkamp ◽  
Bassam Alrifaee
Keyword(s):  
Trajectory Planning ◽  
Autonomous Vehicles ◽  
Optimization Problems ◽  
Receding Horizon Control ◽  
Graph Search ◽  
Finite State Automaton ◽  
Receding Horizon ◽  
Nonconvex Optimization Problems ◽  
Control Approach ◽  
Finite State

<pre>It is hard to find the global optimum to general nonlinear, nonconvex optimization problems in reasonable time. This paper presents a method to transfer the receding horizon control approach, where nonlinear, nonconvex optimization problems are considered, into graph-search problems. Specifically, systems with symmetries are considered to transfer system dynamics into a finite state automaton. In contrast to traditional graph-search approaches where the search continues until the goal vertex is found, the transfer of a receding horizon control approach to graph-search problems presented in this paper allows to solve them in real-time. We proof that the solutions are recursively feasible by restricting the graph search to end in accepting states of the underlying finite state automaton. The approach is applied to trajectory planning for multiple networked and autonomous vehicles. We evaluate its effectiveness in simulation as well as in experiments in the Cyber-Physical Mobility Lab, an open source platform for networked and autonomous vehicles. We show real-time capable trajectory planning with collision avoidance in experiments on off-the-shelf hardware and code in MATLAB for two vehicles.</pre>

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