Affiliation:
1. Institute for Energy Systems, Energy Efficiency and Energy Economics , TU Dortmund University , Dortmund , Germany
Abstract
Abstract
Model Predictive Control (MPC) for {networked, cyber-physical, multi-agent} systems requires numerical methods to solve optimal control problems while meeting communication and real-time requirements. This paper presents an introduction on six distributed optimization algorithms and compares their properties in the context of distributed MPC for linear systems with convex quadratic objectives and polytopic constraints. In particular, dual decomposition, the alternating direction method of multipliers, a distributed active set method, an essentially decentralized interior point method, and Jacobi iterations are discussed. Numerical examples illustrate the challenges, the prospect, and the limits of distributed MPC with inexact solutions.
Subject
Electrical and Electronic Engineering,Computer Science Applications,Control and Systems Engineering
Reference41 articles.
1. Bertsekas, D.P. 1999. Nonlinear Programming. Athena Scientific, Belmont.
2. Bertsekas, D.P. and Tsitsiklis, J.N. 1989. Parallel and Distributed Computation: Numerical Methods, vol. 23. Prentice Hall, Englewood Cliffs, NJ.
3. Bestler, A. and K. Graichen. 2019. Distributed model predictive control for continuous-time conlinear systems based on suboptimal ADMM. Optimal Control Appl. Methods 40(1): 1–23.
4. Boyd, S., N. Parikh, E. Chu, B. Peleato and J. Eckstein. 2011. Distributed optimization and statistical learning via the alternating direction method of multipliers. Found. Trends Mach. Learn. 3(1): 1–122.
5. Braun, P. and L. Grüne. 2018. Verteilte Optimierung: Anwendungen in der Modellprädiktiven Regelung Automatisierungstechnik 66(11): 939–949.
Cited by
6 articles.
订阅此论文施引文献
订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献