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

Alamo, 2015, Randomized methods for design of uncertain systems: Sample complexity and sequential algorithms, Automatica, 52, 160, 10.1016/j.automatica.2014.11.004 Bernardini, 2009, Scenario-based model predictive control of stochastic constrained linear systems, 6333 Calafiore, 2005, Uncertain convex programs: randomized solutions and confidence levels, Mathematical Programming, 102, 25, 10.1007/s10107-003-0499-y Calafiore, 2010, Random convex programs, SIAM Journal on Optimization, 20, 3427, 10.1137/090773490 Calafiore, 2006, The scenario approach to robust control design, IEEE Transactions on Automatic Control, 51, 742, 10.1109/TAC.2006.875041 Calafiore, 2013, Robust model predictive control via scenario optimization, IEEE Transactions on Automatic Control, 56, 219, 10.1109/TAC.2012.2203054 Campi, 2008, The exact feasibility of randomized solutions of uncertain convex programs, SIAM Journal on Optimization, 19, 1211, 10.1137/07069821X Chatterjee, 2015, On stability and performance of stochastic predictive control techniques, IEEE Transactions on Automatic Control, 60, 509, 10.1109/TAC.2014.2335274 Chisci, 2001, Systems with persistent disturbances: predictive control with restricted constraints, Automatica, 37, 1019, 10.1016/S0005-1098(01)00051-6 Di Cairano, 2012, An industry perspective on MPC in large volumes applications: Potential benefits and open challenges, 52 Forbes, 2015, Model predictive control in industry: Challenges and opportunities, IFAC-PapersOnLine, 48, 531, 10.1016/j.ifacol.2015.09.022 Goulart, 2006, Optimization over state feedback policies for robust control with constraints, Automatica, 42, 523, 10.1016/j.automatica.2005.08.023 Grammatico, 2016, A scenario approach for non-convex control design, IEEE Transactions on Automatic Control, 61, 334 Grüne, 2011, Nonlinear model predictive control: Theory and algorithms, 10.1007/978-0-85729-501-9_3 Kang, 2015, Decomposition via ADMM for scenario-based model predictive control, 1246 Kouvaritakis, 2010, Explicit use of probabilistic distributions in linear predictive control, Automatica, 46, 1719, 10.1016/j.automatica.2010.06.034 Limon, 2006, On the stability of constrained MPC without terminal constraint, IEEE Transactions on Automatic Control, 51, 832, 10.1109/TAC.2006.875014 Lorenzen, M., Dabbene, F., Tempo, R., & Allgower, F. (2015). Constraint-tightening and stability in stochastic model predictive control. arXiv cs.SY math.OC (1401893). Mayne, 2013, An apologia for stabilising conditions in model predictive control, International Journal of Control, 86, 2090, 10.1080/00207179.2013.813647 Mayne, 2014, Model predictive control: recent developments and future promise, Automatica, 50, 2967, 10.1016/j.automatica.2014.10.128 Mayne, 2011, Tube based robust nonlinear model predictive control, International Journal of Robust and Nonlinear Control, 21, 1341, 10.1002/rnc.1758 Mayne, 2001, Robustifying model predictive control of constrained linear systems, Electronic Letters, 37, 1422, 10.1049/el:20010951 Mayne, 2000, Constrained model predictive control: stability and optimality, Automatica, 36, 789, 10.1016/S0005-1098(99)00214-9 Mayne, 2005, Robust model predictive control of constrained linear systems with bounded disturbances, Automatica, 41, 219, 10.1016/j.automatica.2004.08.019 Pannocchia, 2011, Conditions under which suboptimal nonlinear MPC is inherently robust, Systems and Control Letters, 60, 747, 10.1016/j.sysconle.2011.05.013 Primbs, 2009, Stochastic receding horizon control of constrained linear systems with state and control multiplicative noise, IEEE Transactions on Automatic Control, 54, 221, 10.1109/TAC.2008.2010886 Prnadini, 2012, A randomized approach to stochastic model predictive control., 7315 Qin, 2003, A survey of industrial model predictive control technology, Control Engineering Practice, 11, 733, 10.1016/S0967-0661(02)00186-7 Raković, 2005, Invariant approximations of the minimal robustly positively invariant sets, IEEE Transactions on Automatic Control, 50, 406, 10.1109/TAC.2005.843854 Rossiter, 1998, A numerically robust state-space approach to stable-predictive control strategies, Automatica, 34, 65, 10.1016/S0005-1098(97)00171-4 Schildbach, 2015, Scenario model predictive control for lane change assistance on highways, 611 Scokaert, 1999, Suboptimal model predictive control (feasibility implies stability), IEEE Transactions on Automatic Control, 44, 648, 10.1109/9.751369 Tempo, 2013, Randomized Algorithms for analysis and control of uncertain systems: With applications Zhang, 2014, Randomized nonlinear MPC for uncertain control-affine systems with bounded closed-loop control violations, 1649 Zhang, 2015, On the sample size of random convex programs with structured dependence on the uncertainty, Automatica, 60, 182, 10.1016/j.automatica.2015.07.013 Zhang, 2013, Stochastic model predictive control using a combination of randomized and robust optimization, 7740