Double-layered nonlinear model predictive control based on Hammerstein–Wiener model with disturbance rejection

Abstract

This paper presents the double-layered nonlinear model predictive control method for a continuously stirred tank reactor and a pH neutralization process that are subject to input disturbances and output disturbances at the same time. The nonlinear systems can be described as a Hammerstein -Wiener model. Furthermore, two nonlinear parts of the Hammerstein -Wiener model should be transformed into linear combination of known input and unknown disturbances, respectively. By taking advantage of Kalman filter, disturbances and states can be estimated. The estimated disturbances and states can be considered to calculate steady-state target in steady-state target calculation layer. Moreover, the state feedback control law can be obtained in dynamic control layer. A simple proof for offset-free control is given in the proposed method. The simulation results show that the controlled variable can achieve the offset-free control. It can be seen that the proposed method has better disturbance rejection performance, strong robustness and practical value.

Coupling dynamic modelling and parameter identification of a flexible manipulator system with harmonic drive

“…The transfer function H(s) is modelled as equation (14), to which the input and output are G coup and u, respectively…”

“…When the excitation voltage is set as binary pseudorandom M-sequence signal shown in Figure 6, the strain voltage V t (t) is measured, and the result is illustrated in Figure 13. Since the relationship between coupling vibration torque G coup (t) and V t (t) is linear, the relationship between V t (t) and u(t) can be also described by equation (14). The u(t) illustrated in Figure 7 is set as input, the V t (t) illustrated in Figure 13, and then the Identification Toolbox of MATLAB 26 is applied to identify the model of transfer function from u(t) to V t (t).…”

“…Ahmad et al 13 proposed a composite fuzzy logic control method for flexible manipulator. Cai et al 14 proposed a nonlinear model which can be applied for flexible manipulator. Xu and Liang 15 proposed an electromagnetic harmonic movable tooth-drive system, which includes a comprehensive study about harmonic character.…”

Modeling and parameter learning method for the Hammerstein–Wiener model with disturbance

“…The Hammerstein and Wiener models represent the most familiar models of the nonlinear block-oriented models. The extension of the Hammerstein and Wiener models is the Hammerstein–Wiener model, which constituted by one dynamic linear block surrounded by two static nonlinear blocks has a great flexibility for describing practical nonlinear systems, such as electric arc furnace system, 19 pH neutralization process, 20,21 continuous stirred tank reactor (CSTR), 22 and fermentation bioreactor system. 23 There is an extensive body of research with regard to various learning algorithms of the Hammerstein–Wiener model, mainly including over-parameterization method, 24 subspace method, 23 blind method, 25 iterative method, 26,27 multi-signal based method, 28 and maximum likelihood method.…”

Model predictive control and estimation of managed pressure drilling using a real-time high fidelity flow model

“…The simplest of these models may be termed gray box models, which incorporate linear transfer function models and nonlinearity blocks to describe relationships between manipulated variables and system behavior. These models have been derived empirically and may take the form of a nonlinear Hammerstein-Wiener model, for example [20,21,22,23].…”