Robust constrained receding-horizon predictive control via bounded data uncertainties

Ramos, C.; Martínez, M.; Sanchis, Javier; Salcedo, J. V.

doi:10.1016/j.matcom.2008.06.002

Cited by 9 publications

(20 citation statements)

References 47 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…As first step, using the GGP method we will solve the optimization problem described by (20). Firstly, we make off-line a translation for all negative variables of the signomial −J following (14).…”

Section: The Rmpc Algorithmmentioning

confidence: 99%

“…This type of MPC has been thoroughly investigated for many years (see e.g [3][4][5][6][7][8][9][10][11][12][13][14][15][16][17][18][19]). One widely used technique for improving robustness in MPC consists of the min-max optimization [20] where a quadratic cost function is minimized with respect to its worst-case, the latter being taken over the set of all possible plant uncertainties. The difference between the existing RMPC methods resides, generally, in the choice of the model.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

A GGP approach to solve non convex min-max predictive controller for a class of constrained MIMO systems described by state-space models

Kheriji

Bouani

Ksouri

2011

Int. J. Control Autom. Syst.

View full text Add to dashboard Cite

This paper proposes a new method to solve non convex min-max predictive controller for a class of constrained linear Multi Input Multi Output (MIMO) systems. A parametric uncertainty state space model is adopted to describe the dynamic behavior of the real process. Moreover, the output deviation method is used to design the j-step ahead output predictor. The control law is obtained by the resolution of a non convex min-max optimization problem under input constraints. The key idea is to transform the initial non convex optimization problem to a convex one by means of variable transformations. To this end, the Generalized Geometric Programming (GGP) which is a global deterministic optimization method is used. An efficient implementation of this approach will lead to an algorithm with a low computational burden. Simulation results performed on Multi Input Multi Output (MIMO) system show successful set point tracking, constraints satisfaction and good non-zero disturbance rejection.

show abstract

Section: The Rmpc Algorithmmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

A GGP approach to solve non convex min-max predictive controller for a class of constrained MIMO systems described by state-space models

Kheriji

Bouani

Ksouri

2011

Int. J. Control Autom. Syst.

View full text Add to dashboard Cite

show abstract

“…Así, fusionando, por una parte el CRHPC y por otra BDU, se obtiene el controlador CRHPC-BDU (Ramos et al, 2008(Ramos et al, , 2009, que proporciona robustez y estabilidad. Por tanto, la técnica BDU sirve para sintonizar, de un modo guiado, el parámetro de penalización del esfuerzo de control del CRHPC cuando hay incertidumbre.…”

Section: Introductionunclassified

“…Como aportaciones principales de este trabajo, por una parte, se ha profundizado en el CRHPC, ofreciendo leyes de control para el mismo diferentes a la original y la presentada en trabajos previos (Ramos et al, 2009), con diferentes herramientas y comprobando la equivalencia entre ellas. Lo cual sirve de base para plantear también otras leyes de control del CRHPC-BDU, diferentes a la presentada en trabajos previos (Ramos et al, 2008(Ramos et al, , 2009, y que consiguen mejoras en la estabilidad del sistema en presencia de incertidumbre, robustez y tiempo de cómputo.…”

Section: Introductionunclassified

Mejora de la estabilidad, robustez y tiempo de cómputo en CRHPC con BDU

Ramos

Martínez

Sanchis

2013

Revista Iberoamericana de Automática e Informática Industrial R

Self Cite

View full text Add to dashboard Cite

En este artículo, se ofrecen diferentes leyes de control de Control Predictivo Basado en Modelos con el fin de mejorar la estabilidad y robustez. El Constrained Receding-Horizon Predictive Control (CRHPC) proporciona estabilidad (en ausencia de incertidumbre), y la técnica Bounded Data Uncertainties (BDU) proporciona una regularización guiada que mejora el mal condicionamiento y la robustez, resultando en el original CRHPC-BDU. El objetivo de este artículo consiste, por una parte, en ofrecer leyes de control alternativas para el CRHPC, usando diferentes herramientas matemáticas y compararlas.Éstos constituyen la base para extender dichas estrategias al CRHPC-BDU. Así, una de las principales contribuciones consiste en obtener una versión de CRHPC-BDU más robusta y con menor coste computacional.

show abstract

“…control (RHC) [10][11][12][13][14][15][16][17][18], has become the optimal control strategy of choice in process industries that typically involve MJS. Some pioneering works have focused on the utilization of MPC/RHC framework for MJS.…”

mentioning

confidence: 99%

Feedback predictive control for constrained fuzzy systems with Markovian jumps

Wen¹,

Liu²,

Nguang³

2011

Asian Journal of Control

View full text Add to dashboard Cite

In this paper, a feedback model predictive control method is presented to tackle control problems with constrained multivariables for uncertain discretetime nonlinear Markovian jump systems. An uncertain Markovian jump fuzzy system (MJFS) is obtained by employing the Takagi-Sugeno (T-S) fuzzy model to represent a discrete-time nonlinear system with norm bounded uncertainties and Markovain jump parameters. To achieve more generality, the transition probabilities of the Markov chain are assumed to be partly unknown and partly accessible. The predictive formulation adopts an on-line optimization paradigm that utilizes the closed-loop state feedback controller and is solved using the standard semi-definite programming (SDP). To reduce the on-line computational burden, a mode independent control move is calculated at every sampling time based on a stochastic fuzzy Lyapunov function (FLF) and a parallel distributed compensation (PDC) scheme. The robust mean square stability, performance minimization and constraint satisfaction properties are guaranteed under the control move for all admissible uncertainties. A numerical example is given to show the efficiency of the developed approach.

show abstract

Robust constrained receding-horizon predictive control via bounded data uncertainties

Cited by 9 publications

References 47 publications

A GGP approach to solve non convex min-max predictive controller for a class of constrained MIMO systems described by state-space models

A GGP approach to solve non convex min-max predictive controller for a class of constrained MIMO systems described by state-space models

Mejora de la estabilidad, robustez y tiempo de cómputo en CRHPC con BDU

Feedback predictive control for constrained fuzzy systems with Markovian jumps

Contact Info

Product

Resources

About