TR2013-059
Projection-free Parallel Quadratic Programming for Linear Model Predictive Control
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- "Projection-free Parallel Quadratic Programming for Linear Model predictive Control", International Journal of Control, July 2013.BibTeX TR2013-059 PDF Software
- @article{DiCairano2013jul,
- author = {{Di Cairano}, S. and Brand, M. and Bortoff, S.A.},
- title = {Projection-free Parallel Quadratic Programming for Linear Model predictive Control},
- journal = {International Journal of Control},
- year = 2013,
- month = jul,
- url = {https://www.merl.com/publications/TR2013-059}
- }
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- "Projection-free Parallel Quadratic Programming for Linear Model predictive Control", International Journal of Control, July 2013.
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MERL Contacts:
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Research Areas:
Abstract:
A key component in enabling the application of model predictive control (MPC) in fields such as automotive, aerospace and factory automation is the availability of low-complexity fast optimization algorithms to solve the MPC finite horizon optimal control problem in architectures with reduced computational capabilities. In this paper we introduce a projection-free iterative optimization algorithm and discuss its application to linear MPC. The algorithm, originally developed by Brand for non-negative quadratic programs, is based on a multiplicative update rule and it is shown to converge to a fixed point which is the optimum. An acceleration technique based on a projection-free line search is also introduced, to speed-up the convergence to the optimum. The algorithm is applied to MPC through the dual of the quadratic program (QP) formulated from the MPC finite time optimal control problem. We discuss how termination conditions with guaranteed degree of suboptimality can be enforced, and how the algorithm performance can be optimized by pre-computing the matrices in a parametric form.We show computational results of the algorithm in three common case studies and we compare such results with the results obtained by other available free and commercial QP solvers.
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Related News & Events
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NEWS MERL's High-speed optimization algorithms showcased at Mitsubishi Electric Corporation annual R&D Open House Date: February 13, 2014
MERL Contact: Matthew BrandBrief- Mitsubishi Electric Corporation announced its development of advanced optimization algorithms and high-speed calculation methods aimed at optimizing the performance of three practical systems: laser-processing machines for high-speed cutting of sheet metal using the shortest possible trajectories, moon probes achieved with minimized fuel consumption, and particle beam therapies for prompt medical treatments.
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NEWS International Journal of Control: publication by Matthew E. Brand, Scott A. Bortoff and Stefano Di Cairano Date: July 9, 2013
Where: International Journal of Control
MERL Contacts: Stefano Di Cairano; Matthew Brand; Scott A. Bortoff
Research Area: ControlBrief- The article "Projection-free Parallel Quadratic Programming for Linear Model predictive Control" by Di Cairano, S., Brand, M. and Bortoff, S.A. was published in International Journal of Control.