A primal-dual interior-point method for robust optimal control of linear discrete-time systems

This paper describes how to efficiently solve a robust optimal control problem using recently developed primal-dual interior-point methods. One potential application is model predictive control. The optimization problem considered consists of a worst case quadratic performance criterion over a finit...

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Bibliographic Details
Published inIEEE transactions on automatic control Vol. 45; no. 9; pp. 1639 - 1655
Main Author Hansson, A.
Format Journal Article
LanguageEnglish
Published New York IEEE 01.09.2000
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subjects
Constraint optimization
Inequalities
Mathematical analysis
Mathematical model
Mathematical models
Matlab
Optimal control
Optimization
Polynomials
Predictive control
Predictive models
Quadratic programming
Riccati equations
Robust control
Robust stability
Solvers
Workstations
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ISSN0018-9286
1558-2523
DOI10.1109/9.880615

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Summary:This paper describes how to efficiently solve a robust optimal control problem using recently developed primal-dual interior-point methods. One potential application is model predictive control. The optimization problem considered consists of a worst case quadratic performance criterion over a finite set of linear discrete-time models subject to inequality constraints on the states and control signals. The scheme has been prototyped in Matlab. To give a rough idea of the efficiencies obtained, it is possible to solve problems with more than 10 000 primal variables and 40 000 constraints on a workstation. The key to the efficient implementation is an iterative solver in conjunction with a Riccati-recursion invertible pre-conditioner for computing the search directions.
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ISSN:0018-9286
1558-2523
DOI:10.1109/9.880615