Nonlinear predictive control of dynamic systems represented by Wiener–Hammerstein models

@article{awryczuk2016NonlinearPC,
  title={Nonlinear predictive control of dynamic systems represented by Wiener–Hammerstein models},
  author={Maciej Ławryńczuk},
  journal={Nonlinear Dynamics},
  year={2016},
  volume={86},
  pages={1193-1214}
}
This paper is concerned with computationally efficient nonlinear model predictive control (MPC) of dynamic systems described by cascade Wiener–Hammerstein models. The Wiener–Hammerstein structure consists of a nonlinear steady-state block sandwiched by two linear dynamic ones. Two nonlinear MPC algorithms are discussed in details. In the first case the model is successively linearised on-line for the current operating conditions, whereas in the second case the predicted output trajectory of the… Expand
Nonlinear model predictive control based on piecewise linear Hammerstein models
TLDR
This paper develops a nonlinear model predictive control (MPC) algorithm for dynamic systems represented by piecewise linear Hammerstein models that does not require the inversion of static nonlinearity and can directly cope with input constraints even in multivariable systems. Expand
Model predictive control for systems with fast dynamics using inverse neural models.
TLDR
Results show that the proposed approach outperforms the rivaling schemes in terms of response; moreover, it solves the optimization problem in less than one sampling period, thus effectively rendering MPC-based controllers capable of handling systems with fast dynamics. Expand
Two-step output feedback predictive control for Hammerstein systems with networked-induced time delays
  • Jun Wang, B. Ding
  • Computer Science
  • Int. J. Syst. Sci.
  • 2018
TLDR
The time-delay compensation algorithm of two-step output feedback predictive control (TSOFPC) for Hammerstein systems is presented and validated by a numerical example. Expand
Neural network identification in nonlinear model predictive control for frequent and infrequent operating points using nonlinearity measure.
TLDR
A novel optimal identification algorithm is presented, which is highly depended on the nonlinearity of the understudy plant, to train theNonlinear model of the NMPC, selected as a multi-layer perceptron neural network (MLP) which is trained to describe the non Linear behaviour of the non linear dynamic system accurately in the FOP. Expand
Identification of Nonlinear Dynamic Systems Using Fuzzy Hammerstein-Wiener Systems
In this paper, a new fuzzy Hammerstein-Wiener model (FHWM) is developed in order to identify a nonlinear dynamic system operating in a stochastic environment. Wherein more general aspect isExpand
Nonlinear model predictive control based on Nelder Mead optimization method
In this paper, a model predictive control (MPC) scheme based on Hammerstein model is carried on. The use of such nonlinear models complicates the implementation of the MPC in terms of computationalExpand
Filled Function Method for Nonlinear Model Predictive Control
TLDR
The method to use the filled function as a global optimization method to solve the nonconvex optimization problem of the nonlinear model predictive control (NMPC) for Hammerstein model is presented. Expand
WH-EA: An Evolutionary Algorithm for Wiener-Hammerstein System Identification
TLDR
A novel approach to identify Wiener-Hammerstein systems in a single step is proposed based on a customized evolutionary algorithm able to look for the best BLA split, capturing at the same time the process static nonlinearity with high precision. Expand
Data-driven Learning Algorithm of Neural Fuzzy Based Hammerstein-Wiener System
  • Feng Li, Yinsheng Luo, Naibao He, Ya Gu, Qingfeng Cao
  • Computer Science
  • J. Sensors
  • 2021
TLDR
A novel data-driven learning approach of nonlinear system represented by neural fuzzy Hammerstein-Wiener model, which has two static nonlinear blocks represented by two independent neural fuzzy models surrounding a dynamic linear block described by finite impulse response model, is presented. Expand
Design of sign fractional optimization paradigms for parameter estimation of nonlinear Hammerstein systems
TLDR
Novel sign fractional least mean square algorithms are designed for ease in hardware implementation by applying sign function to input data and estimation error corresponding to first and fractional-order derivative terms in weight update mechanism of the standard F-LMS method. Expand
...
1
2
3
...

References

SHOWING 1-10 OF 41 REFERENCES
Nonlinear predictive control for Hammerstein-Wiener systems
This paper discusses a nonlinear Model Predictive Control (MPC) algorithm for multiple-input multipleoutput dynamic systems represented by cascade Hammerstein–Wiener models. The block-orientedExpand
Practical nonlinear predictive control algorithms for neural Wiener models
Abstract This paper describes three nonlinear Model Predictive Control (MPC) algorithms for neural Wiener models. In all algorithms the model or the output trajectory is linearised on-line and usedExpand
Output feedback model predictive control for nonlinear systems represented by Hammerstein-Wiener model
Abstract This paper presents dynamic output feedback model predictive control (DOFMPC) for nonlinear systems represented by a Hammerstein–Wiener model. Compared with a previous work (IET-OFMPC:Expand
Model-based predictive control for Hammerstein?Wiener systems
In this paper a model-based predictive control (MPC) algorithm is presented for Hammerstein?Wiener systems. This type of system consists of a linear dynamic block preceded and followed by a staticExpand
Nonlinear model predictive control of multivariable processes using block-structured models
Block-structured models, such as Wiener or Hammerstein models, have been used in nonlinear model predictive control to reduce the cost of identification and online computation. The solution of aExpand
Computationally efficient nonlinear predictive control based on neural Wiener models
TLDR
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. Expand
Suboptimal nonlinear predictive control based on multivariable neural Hammerstein models
TLDR
A computationally efficient nonlinear Model Predictive Control (MPC) algorithm in which the neural Hammerstein model is used, which gives control performance similar to that obtained in nonlinear MPC, which hinges on non-convex optimization. Expand
Model Predictive Control of Hammerstein Systems with Multivariable Nonlinearities
In this paper, a model predictive control (MPC) design method is proposed for Hammerstein systems with multivariable nonlinearities. Iterative inversion is introduced such that classical linear MPCExpand
A Wiener Neural Network-Based Identification and Adaptive Generalized Predictive Control for Nonlinear SISO Systems
In this study, a Wiener-type neural network (WNN) is derived for identification and control of single-input and single-output (SISO) nonlinear systems. The nonlinear system is identified by the WNN,Expand
On identification of well‐conditioned nonlinear systems: Application to economic model predictive control of nonlinear processes
The focus of this work is on economic model predictive control (EMPC) that utilizes well-conditioned polynomial nonlinear state-space (PNLSS) models for processes with nonlinear dynamics.Expand
...
1
2
3
4
5
...