Multimodel robust control of nonlinear plants: a case study

Nyström, Rasmus H.; Sandström, K.V.; Gustafsson, T.K.; Toivonen, Hannu

doi:10.1016/s0959-1524(98)00040-7

Cited by 24 publications

(19 citation statements)

References 24 publications

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“…al. [8], [7], [21], dealing with the following acid-base system H + , H 3 PO 4 , H 2 PO − 4 , HPO 2− 4 , PO 3− 4 , OH − , which are assumed to be at equilibrium, which is reached instantaneously. There are also a formation of salts (CaHPO 4 and Ca 3 (PO 4 ) 2 ), which may stay in solution as well as precipitate and form a solid phase.…”

Section: Model Of Ph Neutralization Processmentioning

confidence: 99%

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Control of pH neutralization system using nonlinear model predictive control with I-controller

Hermansson

Syafiie

2014

2014 IEEE International Conference on Industrial Engineering and Engineering Management

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In the process industry controlling the pH is considered to be one of the toughest tasks among the most commonly controlled variables. This is due to the nonlinear behavior of the pH and the time dependence of the nonlinearity, requiring an advanced controller. In this paper a multi-model nonlinear model predictive control (MMNMPC) scheme is applied to describe and handle the nonlinearities, were the multi-model description gives a piecewise linear description enabling a simple and swift computation of control moves. MPC implementation requires the knowledge, through measurement or by estimation, of the states of the neutralization system, both creating various problems. Here problem is addressed by including integral action in the controller to compensate for the unmeasured states. The MMNMPC combined with integral action is tested by simulation of a pH system in Matlab and the control structures were applied in Matlab/YALMIP showing good control performance.

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Section: Model Of Ph Neutralization Processmentioning

confidence: 99%

“…The model bank were produced by linearizing around five base points (pH=3, 4, 5, 6 and 7) applying deviation variables for X, X f , X u and Y for each model. The model bank given by [21] is continuous and was discretisized with a sampling time of 0.5 min to give a model form for Eqs. (19)(20) given as…”

Section: Model Of Ph Neutralization Processmentioning

confidence: 99%

Control of pH neutralization system using nonlinear model predictive control with I-controller

Hermansson

Syafiie

2014

2014 IEEE International Conference on Industrial Engineering and Engineering Management

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“…A model reference adaptive control approach is given in [24]. A multimodel robust controller is described in [41].…”

Section: Neutralisation Reactormentioning

confidence: 99%

On-line set-point optimisation and predictive control using neural Hammerstein models

Ławryńczuk

2011

Chemical Engineering Journal

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“…The authors use the multimodel paradigm to describe the uncertainties and derived a convex constraint problem to design the controller. In Nyström et al (1999) a multimodel controller design combined with gain scheduling methods is studied based on a mixed H 2 /H ∞ problem. These approaches require the approximation of the time-delay and suffer from the conservatism related to the existence of a common Lyapunov matrix for all closed loop systems.…”

Section: Introductionmentioning

confidence: 99%

Robust and Gain-Scheduled PID Controller Design for Condensing Boilers by Linear Programming

Oliveira

Karimi

2012

IFAC Proceedings Volumes

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This paper addresses the water temperature control in condensing domestic boilers. The main challenge of this process under the controller design perspective is the fact that the dynamics of condensing boilers are strongly affected by the demanded water flow rate. Two approaches are presented in this paper. First, a robust PI controller is designed that stabilizes and achieves good performance for closed-loop system for a wide range of the water flow rate. Then, it is shown that if the water flow rate information is used to update the controller gains, a technique known as gain-scheduled control, the performance can be significantly improved. Several models of a boiler in different water flow rate are identified in collaboration with Honeywell, and the effectiveness of the results are illustrated by simulation.

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Multimodel robust control of nonlinear plants: a case study

Cited by 24 publications

References 24 publications

Control of pH neutralization system using nonlinear model predictive control with I-controller

Control of pH neutralization system using nonlinear model predictive control with I-controller

On-line set-point optimisation and predictive control using neural Hammerstein models

Robust and Gain-Scheduled PID Controller Design for Condensing Boilers by Linear Programming

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