Wiener model based nonlinear predictive control

Gerkšič, Samo; Juričić, Đani; Strmĉnik, Stanko; Matko, Drago

doi:10.1080/002077200291307

Cited by 47 publications

(23 citation statements)

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“…In particular, Wiener models (WMs) have a special structure that facilitates their application to NMPC (Norquay, Palazoglu, and Romagnoli 1998;Gerksˇicˇ, Juricˇic, Strmcˇnic, and Matko 2000;Lusso´n-Cervantes, Agamennoni, and Figueroa 2003). These models consist in a process with linear dynamics followed by a nonlinear gain (Figure 1), and can represent many of the nonlinearities commonly encountered in industrial processes.…”

Section: Introductionmentioning

confidence: 99%

Robust model predictive control of Wiener systems

Biagiola

Figueroa

2011

International Journal of Control

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Block-oriented models (BOMs) have shown to be appealing and efficient as nonlinear representations for many applications. They are at the same time valid and simple models in a more extensive region than time-invariant linear models. In this work, Wiener models are considered. They are one of the most diffused BOMs, and their structure consists in a linear dynamics in cascade with a nonlinear static block. Particularly, the problem of control of these systems in the presence of uncertainty is treated. The proposed methodology makes use of a robust identification procedure in order to obtain a robust model to represent the uncertain system. This model is then employed to design a model predictive controller. The mathematical problem involved in the controller design is formulated in the context of the existing linear matrix inequalities (LMI) theory. The main feature of this approach is that it takes advantage of the static nature of the nonlinearity, which allows to solve the control problem by focusing only in the linear dynamics. This formulation results in a simplified design procedure, because the original nonlinear model predictive control (MPC) problem turns into a linear one.

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Section: Introductionmentioning

confidence: 99%

Robust model predictive control of Wiener systems

Biagiola

Figueroa

2011

International Journal of Control

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“…Different approaches of Wiener-model identification are found in the literature. The most frequently used is the nonlinear-linear (N-L) approach, which is the most comprehensible and ensures an accurate description of the static nonlinearity [14]. This approach requires steady-state data.…”

Section: Interval Fuzzy Model Used In the Wiener Modelmentioning

confidence: 99%

“…. ; 4) are mapped to the output domain across the fuzzy functions (14) to obtain the membership functions B and B , as shown in Fig. 3 …”

Section: Interval Fuzzy Model Used In the Wiener Modelmentioning

confidence: 99%

Interval Fuzzy Modeling Applied to Wiener Models With Uncertainties

Škrjanc

Blažič

Agamennoni

2005

IEEE Trans. Syst., Man, Cybern. B

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“…In most of the control applications of Wiener Models, the underlying strategy involves the inverse of the nonlinearity, such as in the works by Gerkšič et al (2000), Bloemen et al (2001a) and Baeyens (2000, 2004).…”

Section: Introductionmentioning

confidence: 99%

“…WMs have proved to be useful for applications in several fields, such as in chemical processes (Kalafatis et al, 1995;Pajunen, 1992;Pearson and Pottmann, 2000;Zhu, 1999), biological processes (Korenberg, 1973;Hunter and Korenberg, 1986), communications (Kang et al, 1998;Kang et al, 1999;Cheong et al, 2005) and control (Norquay et al, 1998;Gerkšič et al, 2000;Lussón Cervantes et al, 2003;Biagiola et al, 2004).…”

Section: Introductionmentioning

confidence: 99%

ROBUST CONTROL OF WIENER SYSTEMS: APPLICATION TO A pH NEUTRALIZATION PROCESS

Biagiola

Agamennoni

Figueroa

2016

Braz. J. Chem. Eng.

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-In this paper, the robustness of a typical control scheme for Wiener systems is studied. These systems consist of the cascade connection of a linear time invariant system and a static nonlinearity. To control this kind of systems, several approaches were discussed in the literature. Most of these control schemes involve transformation of the measured variable as well as the setpoint, by the inverse of the nonlinear gain. The approach followed in this work uses the inverse model of the static nonlinear gain, while the uncertainty in the Wiener model is treated as a partitioned problem. The linear block is considered as a parameter-affine-dependent model and, on the other hand, the nonlinear block uncertainty is analyzed as a conic-sector. The robustness analysis is performed using -theory. The results are evaluated on the basis of a simulation of a pH neutralization process.

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Wiener model based nonlinear predictive control

Cited by 47 publications

References 0 publications

Robust model predictive control of Wiener systems

Robust model predictive control of Wiener systems

Interval Fuzzy Modeling Applied to Wiener Models With Uncertainties

ROBUST CONTROL OF WIENER SYSTEMS: APPLICATION TO A pH NEUTRALIZATION PROCESS

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