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An iterative scheme for distributed model predictive control using Fenchel ’ s duality ∗
| Content Provider | Semantic Scholar |
|---|---|
| Author | Doan, Minh Dang Keviczky, Tamás Schutter, Bart De |
| Copyright Year | 2012 |
| Abstract | We present an iterative distributed version of Han’s parall el method for convex optimization that can be used for distributed model predictive c ontrol (DMPC) of industrial processes described by dynamically coupled linear systems . The underlying decomposition technique relies on Fenchel’s duality and allows sub pro lems to be solved using local communications only. We investigate two techniques a imed at improving the convergence rate of the iterative approach and illustrate t he results using a numerical example. We conclude by discussing open issues of the pro posed method and by providing an outlook on research in the field. |
| File Format | PDF HTM / HTML |
| Alternate Webpage(s) | http://www.dcsc.tudelft.nl/~bdeschutter/pub/rep/10_063.pdf |
| Language | English |
| Access Restriction | Open |
| Subject Keyword | Convergence (action) Convex optimization Dimyristoylphosphatidylcholine Distributed algorithm Dual Fenchel's duality theorem Han unification Iteration Iterative method Lagrangian relaxation Linear system Mathematical optimization Mesenchymal Stem Cells Microsoft Outlook for Mac Monte Carlo method Numerical analysis Quadratic programming Rate of convergence |
| Content Type | Text |
| Resource Type | Article |
