A Nonlinear Model Predictive Control System Based on Wiener Model

2006 ◽  
Author(s):  
Z. Shahraeini ◽  
N. Daneshpour ◽  
M.R. Jahed Motlagh ◽  
J. Poshtan
Keyword(s):  
Control System ◽  
Model Predictive Control ◽  
Predictive Control ◽  
Nonlinear Model ◽  
Nonlinear Model Predictive Control ◽  
Wiener Model

scholarly journals Nonlinear model predictive control of a boiler unit: A fault tolerant control study

2012 ◽  
Vol 22 (1) ◽  
pp. 225-237 ◽  
Author(s):  
Krzysztof Patan ◽  
Józef Korbicz
Keyword(s):  
Control System ◽  
Model Predictive Control ◽  
Predictive Control ◽  
Nonlinear Model ◽  
Fault Tolerant ◽  
Fault Tolerant Control ◽  
Nonlinear Model Predictive Control ◽  
Boiler Unit ◽  
Predictive Controller ◽  
One Step

Nonlinear model predictive control of a boiler unit: A fault tolerant control studyThis paper deals with a nonlinear model predictive control designed for a boiler unit. The predictive controller is realized by means of a recurrent neural network which acts as a one-step ahead predictor. Then, based on the neural predictor, the control law is derived solving an optimization problem. Fault tolerant properties of the proposed control system are also investigated. A set of eight faulty scenarios is prepared to verify the quality of the fault tolerant control. Based of different faulty situations, a fault compensation problem is also investigated. As the automatic control system can hide faults from being observed, the control system is equipped with a fault detection block. The fault detection module designed using the one-step ahead predictor and constant thresholds informs the user about any abnormal behaviour of the system even in the cases when faults are quickly and reliably compensated by the predictive controller.

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

2018 ◽  
Vol 51 (7-8) ◽  
pp. 260-275 ◽  
Author(s):  
Hongbin Cai ◽  
Ping Li ◽  
Chengli Su ◽  
Jiangtao Cao
Keyword(s):  
Steady State ◽  
Model Predictive Control ◽  
Predictive Control ◽  
Nonlinear Model ◽  
Disturbance Rejection ◽  
Control Method ◽  
Dynamic Control ◽  
State Feedback Control ◽  
Nonlinear Model Predictive Control ◽  
Wiener Model

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.

scholarly journals Steam Turbine Rotor Stress Control through Nonlinear Model Predictive Control

Energies ◽  
2021 ◽  
Vol 14 (13) ◽  
pp. 3998
Author(s):  
Stefano Dettori ◽  
Alessandro Maddaloni ◽  
Filippo Galli ◽  
Valentina Colla ◽  
Federico Bucciarelli ◽  
...  
Keyword(s):  
Control System ◽  
Model Predictive Control ◽  
Steam Turbine ◽  
Predictive Control ◽  
Nonlinear Model ◽  
Computational Cost ◽  
Soft Sensor ◽  
Nonlinear Model Predictive Control ◽  
Steam Turbines ◽  
Control Logic

The current flexibility of the energy market requires operating steam turbines that have challenging operation requirements such as variable steam conditions and higher number of startups. This article proposes an advanced control system based on the Nonlinear Model Predictive Control (NMPC) technique, which allows to speed up the start-up of steam turbines and increase the energy produced while maintaining rotor stress as a constraint variable. A soft sensor for the online calculation of rotor stress is presented together with the steam turbine control logic. Then, we present how the computational cost of the controller was contained by reducing the order of the formulation of the optimization problem, adjusting the scheduling of the optimizer routine, and tuning the parameters of the controller itself. The performance of the control system has been compared with respect to the PI Controller architecture fed by the soft sensor results and with standard pre-calculated curves. The control architecture was evaluated in a simulation exploiting actual data from a Concentrated Solar Power Plant. The NMPC technique shows an increase in performance, with respect to the custom PI control application, and encouraging results.

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