Journal
OPTIMAL CONTROL APPLICATIONS & METHODS
Volume 32, Issue 3, Pages 298-312Publisher
WILEY
DOI: 10.1002/oca.939
Keywords
dynamic optimization; model predictive control; parameter estimation; optimization software
Categories
Funding
- Research Council KUL [CoE EF/05/006, IOF-SCORES4CHEM, GOA/10/009 (MaNet), GOA/10/11]
- Flemish Government: FWO [G.0452.04, G.0499.04, G.0211.05, G.0226.06, G.0321.06, G.0302.07, G.0320.08, G.0558.08, G.0557.08, G.0588.09, G.0377.09]
- IWT
- Belgian Federal Science Policy Office [IUAP P6/04]
- EU
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In this paper the software environment and algorithm collection ACADO Toolkit is presented, which implements tools for automatic control and dynamic optimization. It provides a general framework for using a great variety of algorithms for direct optimal control, including model predictive control as well as state and parameter estimation. The ACADO Toolkit is implemented as a self-contained C++ code, while the object-oriented design allows for convenient coupling of existing optimization packages and for extending it with user-written optimization routines. We discuss details of the software design of the ACADO Toolkit 1.0 and describe its main software modules. Along with that we highlight a couple of algorithmic features, in particular its functionality to handle symbolic expressions. The user-friendly syntax of the ACADO Toolkit to set up optimization problems is illustrated with two tutorial examples: an optimal control and a parameter estimation problem. Copyright (C) 2010 John Wiley & Sons, Ltd.
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