Model-based predictive control for Hammerstein?Wiener systems

Bloemen, H.H.J.; Boom, T.J.J. van den; Verbruggen, H.B.

doi:10.1080/00207170010014061

Cited by 102 publications

(69 citation statements)

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“…It is a popular nonlinear plant/ process modelling approach for a wide range of biological/ engineering problems [10][11][12][13]. For example, it is a suitable model for signal processing applications involving any nonlinear distortion followed by a linear filter, the modelling of the human heart in order to regulate the heart rate during treadmill exercises [14] and the modelling of hydraulic actuator friction dynamics [15].…”

Section: Introductionmentioning

confidence: 99%

The system identification and control of Hammerstein system using non-uniform rational B-spline neural network and particle swarm optimization

Hong

Chen

2012

Neurocomputing

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a b s t r a c tIn this paper a new system identification algorithm is introduced for Hammerstein systems based on observational input/output data. The nonlinear static function in the Hammerstein system is modelled using a non-uniform rational B-spline (NURB) neural network. The proposed system identification algorithm for this NURB network based Hammerstein system consists of two successive stages. First the shaping parameters in NURB network are estimated using a particle swarm optimization (PSO) procedure. Then the remaining parameters are estimated by the method of the singular value decomposition (SVD). Numerical examples including a model based controller are utilized to demonstrate the efficacy of the proposed approach. The controller consists of computing the inverse of the nonlinear static function approximated by NURB network, followed by a linear pole assignment controller.

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Section: Introductionmentioning

confidence: 99%

The system identification and control of Hammerstein system using non-uniform rational B-spline neural network and particle swarm optimization

Hong

Chen

2012

Neurocomputing

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“…The Hammerstein model, comprising a nonlinear static functional transformation followed by a linear dynamical model, has been applied to nonlinear plant/process modelling in a wide range of biological/engineering problems [10,4,14,2]. Model based control for the Hammerstein system has been well studied [1,3,4].…”

Section: Introductionmentioning

confidence: 99%

“…Model based control for the Hammerstein system has been well studied [1,3,4]. The implementation of model based control for an a priori unknown Hammerstein model requires system identification including modelling and identification of the nonlinear static function.…”

Section: Introductionmentioning

confidence: 99%

B-Spline Neural Networks Based PID Controller for Hammerstein Systems

Hong

İplikçi

Chen

et al. 2012

Communications in Computer and Information Science

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Abstract. A new PID tuning and controller approach is introduced forHammerstein systems based on input/output data. A B-spline neural network is used to model the nonlinear static function in the Hammerstein system. The control signal is composed of a PID controller together with a correction term. In order to update the control signal, the multistep ahead predictions of the Hammerstein system based on the B-spline neural networks and the associated Jacobians matrix are calculated using the De Boor algorithms including both the functional and derivative recursions. A numerical example is utilized to demonstrate the efficacy of the proposed approaches.

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“…These schemes still can not ensure a large stable region in general, and require prior knowledge of the real plant such as order, structure, partial coefficients, etc. Bolemen et al (9) extended their own work (8) which preserves the convex property of the optimization problem, but does not consider input constraints. In order to enlarge the asymptotically stable region for constrained nonlinear systems, Chen and Allgöwer (14) developed a quasi-infinite horizon Nonlinear Model Predictive Control (NMPC) algorithms based on a dual-mode (or two-step) technique, which has opened a new avenue in this fascinating field.…”

Section: Introductionmentioning

confidence: 99%

Model Predictive Control for Block-oriented Nonlinear Systems with Input Constraints

Zhang¹

2011

Advanced Model Predictive Control

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Model-based predictive control for Hammerstein?Wiener systems

Cited by 102 publications

References 0 publications

The system identification and control of Hammerstein system using non-uniform rational B-spline neural network and particle swarm optimization

The system identification and control of Hammerstein system using non-uniform rational B-spline neural network and particle swarm optimization

B-Spline Neural Networks Based PID Controller for Hammerstein Systems

Model Predictive Control for Block-oriented Nonlinear Systems with Input Constraints

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