CORE
🇺🇦
make metadata, not war
Services
Research
Services overview
Explore all CORE services
Access to raw data
API
Dataset
FastSync
Content discovery
Recommender
Discovery
OAI identifiers
OAI Resolver
Managing content
Dashboard
Bespoke contracts
Consultancy services
Support us
Support us
Membership
Sponsorship
Community governance
Advisory Board
Board of supporters
Research network
About
About us
Our mission
Team
Blog
FAQs
Contact us
Chance constrained model predictive control for multi-agent systems with coupling constraints
Authors
,
JP Calliess
UD Hanebeck
D Lyons
Publication date
1 January 2012
Publisher
Abstract
We consider stochastic model predictive control of a multi-agent systems with constraints on the probabilities of inter-agent collisions. First, we discuss a method based on sample average approximation of the collision probabilities to make the stochastic control problem computationally tractable. Empirical results indicate that the complexity of the resulting optimization problem can be too high to be solved under realtime requirements. To reduce the computational burden we propose a second approach. It employs probabilistic bounds to determine regions of increased probability of presence for each agent and introduce constraints for the control problem prohibiting overlap of these regions. We prove that the resulting problem is conservative for the original problem, i.e., every control strategy that is feasible under our new constraints will automatically be feasible for the true original problem. Furthermore, we present simulations demonstrating improved run-time performance of our second approach and compare our stochastic method to robust control. © 2012 AACC American Automatic Control Council)
Similar works
Full text
Available Versions
CUED - Cambridge University Engineering Department
See this paper in CORE
Go to the repository landing page
Download from data provider
oai:generic.eprints.org:709054...
Last time updated on 15/07/2020