Identification of Hammerstein model for bioreactors with input multiplicities

Jyothi, S; Chidambaram, M.

doi:10.1007/s004499900141

Cited by 19 publications

(9 citation statements)

References 6 publications

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“…The findings might have been more interesting if identification of the pH process was made in the presence of disturbances and measurement noises. Jyothi and Chidambaram 44 discussed a closed‐loop Hammerstein model for a continuous bioreactor with input multiplicities. Least square optimization is performed to get the parameters of the model.…”

Section: Nonlinear Model Structuresmentioning

confidence: 99%

Process Modeling, Identification Methods, and Control Schemes for Nonlinear Physical Systems – A Comprehensive Review

2021

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A state‐of‐the‐art review on various identification schemes proposed for the Hammerstein, Wiener, and Volterra systems is presented with respect to the special problems arising in the identification of unknown nonlinear systems. Past and recent developments in the field of nonlinear system identification, parameter estimation, and nonlinear control schemes along with the nonlinearity issues are also deeply investigated. A comprehensive analysis on various parameter estimation approaches and nonlinear control laws are made adding credit to the noteworthy contributions, constructive arguments, and remarkable breakthroughs of researchers in various research fields. The application of most popular nonlinear control strategies for many nonlinear systems in the existing literature are presented spotlighting their merits and major shortcomings. The challenges faced in the nonlinear control field and their emergences to future directions are projected.

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Section: Nonlinear Model Structuresmentioning

confidence: 99%

Process Modeling, Identification Methods, and Control Schemes for Nonlinear Physical Systems – A Comprehensive Review

2021

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“…The complete algorithm [17,19,32] of the RLS method for ARX modeling is given as follows [24] : where Y i is a vector containing the output of the model when it is simulated with the validation input data, Y i v is a vector with the validation output data and y i mean is the mean value of the output y i [16].…”

Section: Identification Of the Mimo Hammerstein Modelmentioning

confidence: 99%

Identification of MIMO Hammerstein models using Singular Value Decomposition approach

Louhichi¹,

Toumi²

2012

IJCA

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In this paper, we present a new approach to identify multivariable Hammerstein systems based on the Singular Value Decomposition (SVD) method. The technique allows for the determination of the memoryless static nonlinearity as well as the estimation of the model parameters of the dynamic Auto-Regressive model with eXogenous input (ARX) part. First of all, an iteration procedure is proposed to identify the parameters of Multi-Input Multi-Output (MIMO) Hammerstein models by using the Recursive Least Squares (RLS) algorithm. Secondly, the obtained parameter estimates of the identification model include the product terms of the parameters of the original systems. So, to separate these parameters of the original parameters from the product terms, the singular value decomposition method is discussed. Finally, a simulation study is performed to demonstrate the effectiveness of the proposed method compared with the existing approaches. General TermsHammerstein systems, System identification.

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“…Whereas the control of strongly non-minimum-phase systems is difficult, we consider a modified PI controller on the basis of an identified Hammerstein model [12]. Through the minimum of the integral square of error (ISE) [22], the tuning formula for the PI controller is determined.…”

Section: Nonlinear Pi Controlmentioning

confidence: 99%

“…We claim that the linearization technique is not an effective control scheme for constrained bioprocesses. To explore the feasible control strategies, Jyothi and Chidambaram [12] used the Hammerstein system representation to identify a first-order plus time-delay model for bioreactors with input multiplicities. Reddy and Chidambaram [13,14] also used a nonlinear control scheme by exploiting the Hammerstein system representation to reinforce the performance of PI controllers in the face of a larger-magnitude disturbance.…”

Section: Introductionmentioning

confidence: 99%

Output regulation of a class of unstructured models of continuous bioreactors: steady-state approaches

Huang

2003

Bioprocess Biosyst Eng

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This article deals with the output regulation of continuous bioreactors in the face of constant disturbances and inverse dynamics. Nonlinear controllers developed on the basis of approximate equilibrium manifolds can almost attenuate measurable or unmeasurable disturbances on the output. This nonlinear feed-forward/feedback control framework without any tuning parameters can be directly implemented to strictly nonlinear systems. Under dynamic actuator constraints and the availability of only output signals for use in the control law, closed-loop simulations demonstrate that the proposed control techniques are superior to a nonlinear PI control scheme based on the identified Hammerstein model.

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Identification of Hammerstein model for bioreactors with input multiplicities

Cited by 19 publications

References 6 publications

Process Modeling, Identification Methods, and Control Schemes for Nonlinear Physical Systems – A Comprehensive Review

Process Modeling, Identification Methods, and Control Schemes for Nonlinear Physical Systems – A Comprehensive Review

Identification of MIMO Hammerstein models using Singular Value Decomposition approach

Output regulation of a class of unstructured models of continuous bioreactors: steady-state approaches

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