Predictive functional control using state estimator-based internal model for ramp disturbance rejection

2016 ◽  
Vol 10 (3) ◽  
pp. 267 ◽  
Author(s):  
Toshiyuki Satoh ◽  
Naoki Saito ◽  
Jun ya Nagase ◽  
Norihiko Saga
Keyword(s):  
Disturbance Rejection ◽  
Internal Model ◽  
State Estimator ◽  
Predictive Functional Control ◽  
Functional Control

Multivariable Predictive Functional Control for the First-Order Plus Time-Delay System

2011 ◽  
Vol 282-283 ◽  
pp. 32-37
Author(s):  
Quan Ling Zhang
Keyword(s):  
Control System ◽  
Steady State ◽  
Disturbance Rejection ◽  
Delay System ◽  
Transfer Function Model ◽  
Time Delay System ◽  
Function Model ◽  
First Order ◽  
Predictive Functional Control ◽  
Functional Control

A multivariable predictive functional control (M-PFC) algorithm based on a two-inputs/two-outputs system with the transfer function model is presented in this paper. A simple and explicit solution of manipulated variables of the control system can be obtained by optimizing the objective function. Simulations of the system applying M-PFC are also provided in here, showing that the presented algorithm has good performance of tracking set-point without steady-state error, disturbance rejection and robustness. Finally, the application of temperature control for the methylamine synthesizing tower is addressed, demonstrating the effectiveness of the proposed PFC algorithm.

Higher Order Predictive Functional Control Versus Dynamical Matrix Control for a Milk Pasteurisation Process: Transfer Function Versus Finite Step Response Internal Models

2013 ◽  
Vol 10 (1) ◽  
pp. 17-27 ◽  
Author(s):  
Tarek Mohamed Khadir ◽  
John Vincent Ringwood
Keyword(s):  
Internal Model ◽  
Industrial Applications ◽  
Higher Order ◽  
Step Response ◽  
Dynamical Matrix ◽  
First Order ◽  
Predictive Functional Control ◽  
Functional Control ◽  
Milk Pasteurisation ◽  
Matrix Control

Abstract Predictive functional control (PFC), a model predictive control algorithm, has been proven to be very successful in a wealth of industrial applications due to its many laudable attribute, such as its simplicity and intuitive appeal. For simple single input single output processes, PFC applications use a first-order plus delay internal model and, as long as such models improve the control over classical control strategies, then their use remains justified. In this paper, a higher order internal PFC model is considered in order to reduce any possible plant-model mismatch, where the internal model is formulated as a series of cascaded or parallel first-order systems. The control approach is compared to a more conventional over parameterized dynamical matrix control (DMC) approach, used extensively for Multi-Input Multi-Output systems in the petrochemical industry. This paper demonstrates the benefits of the PFC higher order formulation for a typical milk pasteurisation plant, with significant improvements in the variances of both controlled and manipulated variables when compared to a first-order PFC. In this aspect, the higher order controller competes well with DMC performances, however, using a much more simpler and compact internal model form.

scholarly journals Comparison of PFC Controllers for Heating Process

2019 ◽  
Vol 95 ◽  
pp. 03005
Author(s):  
Piotr Laszczyk
Keyword(s):  
Experimental Data ◽  
Internal Model ◽  
Dead Time ◽  
Heating Process ◽  
Time Varying ◽  
Industrial Control ◽  
Predictive Functional Control ◽  
Programming Effort ◽  
Functional Control ◽  
And Control

The paper presents research with Predictive Functional Control (PFC) for fluid heating process. Two types of models are proposed and used as internal models for PFC algorithm. The first one includes all nonlinearities that are captured in the process, while the second one includes additionally time varying dead time. Both models were calibrated and verified using experimental data. The paper compares performance of two PFC versions based on mentioned models to indicate the profit of including dead time in model based predictive (MPC) control. Experimental results indicate that including dead time in controller’s internal model result in better performance. Although including varying dead time in controller requires extra programming effort and implementation considerations. All identification and control experiments, which are presented in the paper, were made using experimental installation equipped with industrial control equipment.

Intelligent Control in Low Pressure Methanol Carbonylation CH3COOH Reaction

2011 ◽  
Vol 233-235 ◽  
pp. 1027-1030
Author(s):  
Yan Nian Rui ◽  
Xiao Mei Jiang ◽  
Kai Qiang Liu
Keyword(s):  
Intelligent Control ◽  
Disturbance Rejection ◽  
Reaction Process ◽  
Control Mode ◽  
Reliable System ◽  
System Operation ◽  
Predictive Functional Control ◽  
Regulatory Capacity ◽  
Functional Control ◽  
Reference Input

With catalysis system, carbonylation of methanol liquid is a better method in manufacture of acetic acid. However previous control mode can not well meet technique demands. This paper made some modification on reaction process control based on manufacturing technique. Aimed at much influence upon temperature including uncertainty, cross coupling effects and big delay about model, predictive functional control (PFC) technique combined with PID and feedforward are applied to temperature control of reaction process through supervisory total distributed control which leads to the improvement of regulatory capacity for both reference input tracking and load disturbance rejection. Practical results show stable and reliable system operation.

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