Iterative MILP methods for vehicle control problems

Mixed integer linear programming (MILP) is a powerful tool for planning and control problems because of its modeling capability and the availability of good solvers. However, for large models, MILP methods suffer computationally. In this paper, we introduce two iterative MILP algorithms that address...

Full description

Saved in:
Bibliographic Details
Main Authors: Earl, M.G., D'Andrea, R.
Format: Conference Proceeding
Language:English
Subjects:
Applied sciences
Computer science
control theory
systems
Control system synthesis
Control theory. Systems
Couplings
Exact sciences and technology
Iterative algorithms
Iterative methods
Mathematical programming
Mixed integer linear programming
Nonlinear equations
Operational research and scientific management
Operational research. Management science
Optimal control
Reconnaissance
Sampling methods
Space vehicles
Vehicle dynamics
Online Access:Request full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary:Mixed integer linear programming (MILP) is a powerful tool for planning and control problems because of its modeling capability and the availability of good solvers. However, for large models, MILP methods suffer computationally. In this paper, we introduce two iterative MILP algorithms that address this issue. The first is for obstacle avoidance problems, and the second is for minimum time optimal control problems. The algorithms require fewer binary variables than standard MILP methods and on average require much less computational effort.
ISSN:0191-2216
DOI:10.1109/CDC.2004.1429438