Journal ArticleDOI
Linearizing feedforward-feedback control of pH processes based on the Wiener model
TLDR
In this paper, the authors examined the control of pH processes based on the Wiener model construct (a dynamic linear element representing the mixing dynamics of the process in series with a static nonlinearity representing the titration curve).About:
This article is published in Journal of Process Control.The article was published on 2005-02-01. It has received 66 citations till now. The article focuses on the topics: Feed forward & Control theory.read more
Citations
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Journal ArticleDOI
Practical nonlinear predictive control algorithms for neural Wiener models
TL;DR: In this article, three nonlinear MPC algorithms for neural Wiener models are described for two nonlinear processes: a polymerization reactor and a neutralization reactor. But none of the discussed algorithms do not need an inverse of the steady-state part of the model.
Journal ArticleDOI
Model-free learning control of neutralization processes using reinforcement learning
TL;DR: An alternative approach to pH process control using model-free learning control (MFLC), which is based on reinforcement learning algorithms, which gives a general solution for acid-base systems, yet is simple enough to be implemented in existing control hardware without a model.
Journal ArticleDOI
A recursive parametric estimation algorithm of multivariable nonlinear systems described by Hammerstein mathematical models
Houda Salhi,Samira Kamoun +1 more
TL;DR: The convergence analysis of the RPE algorithm is made using the Lyapunov method and his performance is illustrated using data from an experimental acid–base neutralization process.
Journal ArticleDOI
Computationally efficient nonlinear predictive control based on neural Wiener models
TL;DR: This paper describes a computationally efficient nonlinear model predictive control (MPC) algorithm based on neural Wiener models and its application and a polymerisation process is studied to demonstrate the accuracy and the computational efficiency.
Journal ArticleDOI
A Wiener-type recurrent neural network and its control strategy for nonlinear dynamic applications
Yu-Liang Hsu,Jeen-Shing Wang +1 more
TL;DR: Computer simulations and comparisons with some existing recurrent networks have conducted to confirm the effectiveness and superiority of the proposed Wiener-type network, identification algorithm and control strategy.
References
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Book
Adaptive Control
TL;DR: Benefiting from the feedback of users who are familiar with the first edition, the material has been reorganized and rewritten, giving a more balanced and teachable presentation of fundamentals and applications.
Book
Process Dynamics and Control
TL;DR: This book discusses the development of Empirical Models from Process Data, Dynamic Behavior of First-Order and Second-Order Processes, and Dynamic Response Characteristics of More Complicated Processes.
Journal ArticleDOI
Dynamic modeling and reaction invariant control of pH
TL;DR: A systematic treatment of the dynamics and control of acid-base reactions is presented and a new scheme for adaptive control of pH is proposed, relating pH to the reaction invariant state variables.
Journal ArticleDOI
A new approach to the identification of pH processes based on the Wiener model
TL;DR: In this paper, a frequency sampling filter model is used to represent the dynamic linear element and a simple least-squares algorithm is applied to simultaneously estimate the parameters of the linear subsystem and the inverse static nonlinearity.
Journal ArticleDOI
Identification of Wiener-type nonlinear systems in a noisy environment
TL;DR: In this paper, the frequency response for the linear subsystem and the inverse of the static nonlinearity were used to identify Wiener systems in a noisy environment, where a variety of input excitation signals, including random binary and periodic, can be used with the proposed method.