An application of min–max generalized predictive control to sintering processes

Kim, Yong Ho; Кwon, Wook Hyun

doi:10.1016/s0967-0661(98)00097-5

Cited by 34 publications

(7 citation statements)

References 3 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The following ones are typical. Wook Hyun Kwon modeled the burn-through point control system through an event-based model, and then proposed a output-constrained receding horizon control law to keep BTP stable [7]- [8]. J.Q.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

An ARX model based predictive control strategy for sintering process

Liao

Peng

2009

2009 9th International Conference on Electronic Measurement &Amp; Instruments

View full text Add to dashboard Cite

This paper presents an application of a closed-loop identification method and predictive control strategy for burnthrough point control system in industrial sintering process. Two kinds of identification experiment situation have been discussed. A closed-loop identification strategy has been proposed to build an unbiased MIMO ARX predictive model of burn-through point for sintering process, in order to solve problems existed in open-loop identification. Then predictive control law is employed based on the predictive model to counteract the disturbances and achieve the stability of the burn-through point. Finally the predictive model is proved to be available to describe sintering process and design controller. The real run result show that the proposed method is better and more robust than those of PID control.

show abstract

Section: Introductionmentioning

confidence: 99%

“…Generally, the precise prediction model is the precondition for predictive control algorithm. The event-based model in [7]- [8] and the fuzzy model identified in [9], nevertheless, are inadequate for describe the dynamic, nonlinear and complicated sintering process. This paper is structured as follows.…”

Section: Introductionmentioning

confidence: 99%

An ARX model based predictive control strategy for sintering process

Liao

Peng

2009

2009 9th International Conference on Electronic Measurement &Amp; Instruments

View full text Add to dashboard Cite

show abstract

“…This usually involves the solution of an NP-hard min-max problem ( [10], [16]). As a result, the number of applications of these control strategies is very small, even when there is evidence that they work better than standard predictive controllers in processes with uncertain dynamics [5], [8].…”

Section: Introductionmentioning

confidence: 99%

Min-Max predictive control of a pilot plant using a QP approach

Gruber

Ramı́rez

Álamo

et al. 2008

2008 47th IEEE Conference on Decision and Control

View full text Add to dashboard Cite

The practical implementation of Min-Max MPC (MMMPC) controllers is limited by the computational burden required to compute the control law. This problem can be circumvented by using approximate solutions or upper bounds of the worst possible case of the performance index. In a previous work, the authors presented a computationally efficient MMMPC control strategy in which a close approximation of the solution of the min-max problem is computed using a quadratic programming problem. In this paper, this approach is validated through its application to a pilot plant in which the temperature of a reactor is controlled. The pilot plant is operated with a Simatic IT SCADA system. The controller has been implemented in Matlab and connected to the SCADA using OPC. Realistic values of the parameters of the MMMPC controller have been used. The behavior of the system and the controller is illustrated by means of experimental results.

show abstract

“…Only a few applications to plants with slow dynamics [6] or complex simulated models [7] have been reported. For moderate fast dynamics the min-max problem can be solved numerically only when the number of extreme realizations of the uncertainty is relatively low.…”

Section: Introductionmentioning

confidence: 99%

Min-Max MPC based on a computationally efficient upper bound of the worst case cost

Ramı́rez

Álamo

Camacho

et al. 2006

Journal of Process Control

View full text Add to dashboard Cite

Min-Max MPC (MMMPC) controllers [1] suffer from a great computational burden which limits their applicability in the industry. Sometimes upper bounds of the worst possible case of a performance index have been used to reduce the computational burden. This paper proposes a computationally efficient MMMPC control strategy in which the worst case cost is approximated by an upper bound based on a diagonalization scheme. The upper bound can be computed with O(n 3 ) operations and using only simple matrix operations. This implies that the algorithm can be coded easily even in non mathematical oriented programming languages such as those found in industrial embedded control hardware. A simulation example is given in the paper.

show abstract

An application of min–max generalized predictive control to sintering processes

Cited by 34 publications

References 3 publications

An ARX model based predictive control strategy for sintering process

An ARX model based predictive control strategy for sintering process

Min-Max predictive control of a pilot plant using a QP approach

Min-Max MPC based on a computationally efficient upper bound of the worst case cost

Contact Info

Product

Resources

About