Proceedings ArticleDOI
Stochastic Programming Applied to Model Predictive Control
D.M. de la Penad,Alberto Bemporad,Teodoro Alamo +2 more
- pp 1361-1366
TLDR
This paper takes a different route to solve MPC problems under uncertainty and shows that this formulation guarantees robust constraint fulfillment and that the expected value of the optimum cost function of the closed loop system decreases at each time step.Abstract:
Many robust model predictive control (MPC) schemes are based on min-max optimization, that is, the future control input trajectory is chosen as the one which minimizes the performance due to the worst disturbance realization In this paper we take a different route to solve MPC problems under uncertainty Disturbances are modelled as random variables and the expected value of the performance index is minimized The MPC scheme that can be solved using Stochastic Programming (SP), for which several efficient solution techniques are available We show that this formulation guarantees robust constraint fulfillment and that the expected value of the optimum cost function of the closed loop system decreases at each time stepread more
Citations
More filters
Journal ArticleDOI
Stochastic Model Predictive Control: An Overview and Perspectives for Future Research
TL;DR: In this article, a model predictive control (MPC) approach is proposed to solve an open-loop constrained optimal control problem (OCP) repeatedly in a receding-horizon manner, where the OCP is solved over a finite sequence of control actions at every sampling time instant that the current state of the system is measured.
Journal ArticleDOI
Multi-stage nonlinear model predictive control applied to a semi-batch polymerization reactor under uncertainty
TL;DR: In this paper, the authors present a robust non-conservative nonlinear model predictive control (MPC) approach based on the representation of the evolution of the uncertainty by a scenario tree, and leads to a non-ervative robust control of the uncertain plant because the adaptation of future inputs to new information is taken into account.
Journal ArticleDOI
The scenario approach for Stochastic Model Predictive Control with bounds on closed-loop constraint violations
TL;DR: A novel SCMPC method can be devised for general linear systems with additive and multiplicative disturbances, for which the number of scenarios is significantly reduced.
Journal ArticleDOI
Stochastic Receding Horizon Control of Constrained Linear Systems With State and Control Multiplicative Noise
J. A. Primbs,Chang Hwan Sung +1 more
TL;DR: This work develops a receding horizon control approach to stochastic linear systems with control and state multiplicative noise that also contain constraints that is solved as a semi-definite programming problem.
Proceedings ArticleDOI
A stochastic model predictive control approach for series hybrid electric vehicle power management
TL;DR: In this paper, a stochastic model predictive control (SMPC) is used for power management in vehicles equipped with advanced hybrid powertrains, where the power demand from the driver is modeled as a Markov chain estimated on several driving cycles and used to generate scenarios in the SMPC law.
References
More filters
BookDOI
Introduction to Stochastic Programming
John R. Birge,Franois Louveaux +1 more
TL;DR: This textbook provides a first course in stochastic programming suitable for students with a basic knowledge of linear programming, elementary analysis, and probability to help students develop an intuition on how to model uncertainty into mathematical problems.
Journal ArticleDOI
Robust constrained model predictive control using linear matrix inequalities
TL;DR: This paper presents a new approach for robust MPC synthesis that allows explicit incorporation of the description of plant uncertainty in the problem formulation, and shows that the feasible receding horizon state-feedback control design robustly stabilizes the set of uncertain plants.
Journal ArticleDOI
Optimization under uncertainty: state-of-the-art and opportunities
TL;DR: This paper reviews theory and methodology that have been developed to cope with the complexity of optimization problems under uncertainty and discusses and contrast the classical recourse-based stochastic programming, robust stochastics programming, probabilistic (chance-constraint) programming, fuzzy programming, and stochastically dynamic programming.