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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...

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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
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creator	Earl, M.G. D'Andrea, R.
description	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.
doi_str_mv	10.1109/CDC.2004.1429438
format	conference_proceeding
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source	IEEE Electronic Library (IEL) Conference Proceedings
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
title	Iterative MILP methods for vehicle control problems
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