Model-based predictive control for Hammerstein systems

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

doi:10.1109/cdc.2001.914719

Cited by 29 publications

(27 citation statements)

References 9 publications

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“…(22), if rank(Θ ac )=γ , then, with the identification pairs (â j ,ĉ j ) obtained by Eqs. (8) and (9) and the identification error index defined by Eq. (10), one has e 1 > e 2 > ··· > e γ = 0.…”

Section: Theoremmentioning

confidence: 99%

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

Zhang¹

2011

Advanced Model Predictive Control

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Section: Theoremmentioning

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 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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“…A popular treatment of the control of the Hammerstein model is to remove the nonlinearity via an inversion [9,4,19]. In this study, the controller consists of computing the inverse of the nonlinear static function approximated by NURB, followed by a linear pole assignment controller.…”

Section: Introductionmentioning

confidence: 99%

PSO Assisted NURB Neural Network Identification

Hong

Chen

2012

Lecture Notes in Computer Science

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Abstract.A system identification algorithm is introduced for Hammerstein systems that are modelled using a non-uniform rational B-spline (NURB) neural network. The proposed algorithm 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 linear pole assignment controller are utilized to demonstrate the efficacy of the proposed approach.

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Model-based predictive control for Hammerstein systems

Cited by 29 publications

References 9 publications

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

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

B-Spline Neural Networks Based PID Controller for Hammerstein Systems

PSO Assisted NURB Neural Network Identification

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