Robust and stochastic model predictive control: Are we going in the right direction?

作者: David Mayne

DOI: 10.1016/J.ARCONTROL.2016.04.006

关键词: Industrial engineeringProcess (engineering)Model predictive controlData miningEngineeringStochastic model predictive control

摘要: Motivated by requirements in the process industries, the largest user of model predictive control, we re-examine some features of recent research on this topic. We suggest that some …

参考文章(37)
Georg Schildbach, Francesco Borrelli, Scenario model predictive control for lane change assistance on highways ieee intelligent vehicles symposium. pp. 611- 616 ,(2015) , 10.1109/IVS.2015.7225752
Jia Kang, Arvind U. Raghunathan, Stefano Di Cairano, Decomposition via ADMM for scenario-based Model Predictive Control advances in computing and communications. pp. 1246- 1251 ,(2015) , 10.1109/ACC.2015.7170904
Xiaojing Zhang, Sergio Grammatico, Georg Schildbach, Paul Goulart, John Lygeros, On the sample size of random convex programs with structured dependence on the uncertainty Automatica. ,vol. 60, pp. 182- 188 ,(2015) , 10.1016/J.AUTOMATICA.2015.07.013
Debasish Chatterjee, John Lygeros, Stability and performance of stochastic predictive control arXiv: Systems and Control. ,(2013) , 10.1109/TAC.2014.2335274
Giuseppe Calafiore, Fabrizio Dabbene, Roberto Tempo, Randomized Algorithms for Analysis and Control of Uncertain Systems ,(2004)
M. C. Campi, S. Garatti, The Exact Feasibility of Randomized Solutions of Uncertain Convex Programs Siam Journal on Optimization. ,vol. 19, pp. 1211- 1230 ,(2008) , 10.1137/07069821X
D.Q. Mayne, J.B. Rawlings, C.V. Rao, P.O.M. Scokaert, Survey Constrained model predictive control: Stability and optimality Automatica. ,vol. 36, pp. 789- 814 ,(2000) , 10.1016/S0005-1098(99)00214-9
D.Q. Mayne, M.M. Seron, S.V. Raković, Robust model predictive control of constrained linear systems with bounded disturbances Automatica. ,vol. 41, pp. 219- 224 ,(2005) , 10.1016/J.AUTOMATICA.2004.08.019
David Mayne, An apologia for stabilising terminal conditions in model predictive control International Journal of Control. ,vol. 86, pp. 2090- 2095 ,(2013) , 10.1080/00207179.2013.813647