4 edition of **Stochastic Inequality Constrained Closed-loop Model Predictive Control** found in the catalog.

- 291 Want to read
- 3 Currently reading

Published
**September 30, 2004**
by Delft Univ Pr
.

Written in English

- Engineering - Chemical & Biochemical,
- Technology,
- Science/Mathematics

The Physical Object | |
---|---|

Format | Paperback |

Number of Pages | 208 |

ID Numbers | |

Open Library | OL12803797M |

ISBN 10 | 904072489X |

ISBN 10 | 9789040724893 |

OCLC/WorldCa | 57541049 |

approach for stochastic model predictive control with bounds on closed-loop constraint violations. Automatica, 50(12) – , [6]T. Brudigam, J. Teutsch, D. Wollherr, and M. Leibold. Combined¨ robust and stochastic model predictive control for models of different granularity, arXiv: Stochastic Model Predictive Control Causal State-Feedback Control Stochastic Finite Horizon Control 'Solution' Via Dynamic Professor Boyd is the author of many research articles and three books: Linear Controller Design: Limits of Performance (with Craig Barratt, ), Linear Matrix Inequalities in System and Control Theory (with L. El.

2 days ago Model Predictive Control: Classical, Robust, and Stochastic [Bookshelf] Abstract: Model predictive control (MPC) has become a dominant advanced control framework that has made a tremendous impact on both the academic and industrial control communities. Stochastic Nonlinear Model Predictive Control with Probabilistic Constraints Ali Mesbah 1, Stefan Streif; 2, Rolf Findeisen, and Richard D. Braatz Abstract Stochastic uncertainties are ubiquitous in complex dynamical systems and can lead to undesired variability of system outputs and, therefore, a notable degradation of closed-loop performance.

We develop appropriate notions of invariance and stability for such systems and provide terminal conditions for stochastic model predictive control (SMPC) that guarantee mean-square stability and robust constraint fulfillment of the Markovian switching system in closed-loop with the SMPC law under very weak assumptions. The main idea of Model Predictive Control (MPC) is to use a model of the plant to predict the future evolution of the system [15]. At each sampling time, an optimal control problem is solved over a ﬁnite horizon. The optimal command signal is applied to the process only during the following sampling interval. At the next time step a new.

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The support rank of the joint chance constraint X is easily obtained as ρ 1 = 2. Fig. 3 depicts a phase plot of the closed-loop system trajectory, for two admissible sample-removal pairs (a) (19,0) and (b) (,), corresponding to ε = 10 %.Instances in which the state trajectory leaves X are indicated with a cross.

Note that the distributions are centered around a similar mean in both Cited by: Model predictive control (MPC) is a popular strategy which has been widely adopted in industry as an effective means of dealing with multivariable constrained control problems [1], [2].

The idea behind MPC is to obtain the control signal by solving at each sampling time an open-loop nite-horizon optimal control problem based on a given. Stochastic model predictive control for constrained discrete-time that guarantee mean-square stability and robust constraint fulfillment of the Markovian switching system in closed-loop with the SMPC law under very weak assumptions.

Balakrishnan, M. MorariRobust constrained model predictive control using linear matrix inequalities Cited by: This paper considers linear discrete-time systems with additive disturbances, and designs a Model Predictive Control (MPC) law to minimise a quadratic cost function subject to a chance constraint.

This paper investigates stochastic stabilization procedures based on quadratic and piecewise linear Lyapunov functions for discrete-time linear systems aff Stabilizing Model Predictive Control of Stochastic Constrained Linear Systems - IEEE Journals & MagazineCited by: 1.

Introduction. Model Predictive Control (MPC) is nowadays a standard in many industrial contexts, see e.g., due to its ability to cope with complex control problems and to the availability of theoretical results guaranteeing feasibility and stability properties, reasons have motivated the many efforts devoted to develop MPC algorithms robust with respect to unknown, but.

Model Predictive Control describes the development of tractable algorithms for uncertain, stochastic, constrained systems. The starting point is classical predictive control and the appropriate formulation of performance objectives and constraints to provide guarantees of closed-loop.

An MPC model generates control commands by solving an open-loop finite-horizon constrained optimal control within a prediction horizon, implementing the first time step only in an iterative way (Bertsekas, ; Rawlings and Mayne, ).

Most existing MPC-based CACC models focus on free-flow or light traffic conditions in a deterministic system. states.

A stochastic model predictive control (SMPC) design approach is proposed to optimize closed-loop performance while enforcing constraints. Conditions for stochastic convergence and robust constraints fulﬁllment of the closed-loop system are enforced by solving linear matrix inequality problems off line.

Abstract: The topic of this paper is model predictive control (MPC) for max-plus linear systems with stochastic uncertainties the distribution of which is supposed to be known. We consider linear constraints on the inputs and the outputs. Due to the uncertainties, these linear constraints are formulated as probabilistic or chance constraints, i.e., the constraints are required to be satisfied.

A stochastic model predictive control (SMPC) design approach is proposed to optimize closed-loop performance while enforcing constraints.

Conditions for stochastic convergence and robust constraints fulfillment of the closed-loop system are enforced by solving linear matrix inequality problems off line.

Stochastic inequality constrained closed-loop model-based predictive control of MW-class wind generating system in the electric power supply. D.H. van Hessem, O.H. BosgraStochastic closed-loop model predictive control of continuous nonlinear chemical processes Journal of Process Control, 16.

Abstract: In this paper we propose a stochastic model predictive control (MPC) formulation based on scenario generation for linear systems affected by discrete multiplicative disturbances. By separating the problems of (1) stochastic performance, and (2) stochastic stabilization and robust constraints fulfillment of the closed-loop system, we aim at obtaining a less conservative control.

Recent developments in stochastic MPC provided guarantees of closed loop stability and satisfaction of probabilistic and hard constraints. However the required computation can be formidable for anything other than short prediction horizons. This difficulty is removed in the current paper through the use of tubes of fixed cross-section and variable scaling.

A model describing the evolution of. Model predictive control is a popular control approach for multivariable systems with important process constraints.

The presence of significant stochastic uncertainties can however lead to closed. tic Model Predictive Control (SMPC) framework for stochastic constrained linear systems was proposed.

The authors impose a stochastic Lyapunov decrease condi-tion for the rst step of the SMPC algorithm that is ro-bust with respect to constraint enforcement, and allows to guarantee mean-square stability and robust invari.

BibTeX @MISC{Hessem04stochasticinequality, author = {Dennis Harald Van Hessem}, title = {Stochastic inequality constrained closed-loop model predictive control -- with application to chemical process operation}, year = {}}.

Abstract—In this paper we evaluate closed-loop stochastic model predictive control techniques on a nonlinear high density polyethylene ﬂuidized bed example. Closed-loop MPC is a control strategy in which one optimizes feedforward signals while maintaining back-off to inequality constraints on the process variables.

This back-off is kept. However, as explained above, there are many more properties that capture closed-loop performance. One such property, particularly important for constrained model predictive control (MPC) systems, is the satisfaction of various inequality constraints.

Inequality constrained MPC systems rely on the on-line optimization of an objective function over. Abstract: Stochastic uncertainties in complex systems lead to variability of system states, which can degrade the closed-loop performance.

This paper presents a model predictive control approach for a class of nonlinear systems with unbounded stochastic uncertainties. The control approach aims at shaping the probability distribution function of stochastic states, while satisfying input and.Stochastic Model Predictive Control with Discounted Probabilistic Constraints Shuhao Yan, Paul Goulart and Mark Cannon Abstract—This paper considers linear discrete-time systems with additive disturbances, and designs a Model Predictive Control (MPC) law to minimise a quadratic cost function subject to a chance constraint.

The chance.Stochastic Model Predictive Control. by Ali procedure for approximate explicit model predictive control for constrained nonlinear systems described in linear parameter-varying (LPV) form.