On-line startup of a distillation column using generic model control

1993 ◽  
Vol 17 ◽  
pp. S349-S354 ◽  
Author(s):  
M. Barolo ◽  
G.B. Guarise ◽  
S. Rienzi ◽  
A. Trotta
Keyword(s):  
Distillation Column ◽  
Generic Model ◽  
Model Control ◽  
On Line ◽  
Generic Model Control

On-line dynamic optimization integrated with generic model control of a batch crystallizer

2008 ◽  
Vol 14 (4) ◽  
pp. 442-448 ◽  
Author(s):  
Woranee Paengjuntuek ◽  
Paisan Kittisupakorn ◽  
Amornchai Arpornwichanop
Keyword(s):  
Dynamic Optimization ◽  
Generic Model ◽  
Model Control ◽  
On Line ◽  
Generic Model Control

scholarly journals A Gain-Scheduling PI Control Based on Neural Networks

Complexity ◽  
2017 ◽  
Vol 2017 ◽  
pp. 1-8 ◽  
Author(s):  
Stefania Tronci ◽  
Roberto Baratti
Keyword(s):  
Controller Design ◽  
Neural Model ◽  
Gain Scheduling ◽  
Generic Model ◽  
Continuous Stirred Tank Reactor ◽  
Single Input Single Output ◽  
Single Output ◽  
Model Control ◽  
On Line ◽  
Generic Model Control

This paper presents a gain-scheduling design technique that relies upon neural models to approximate plant behaviour. The controller design is based on generic model control (GMC) formalisms and linearization of the neural model of the process. As a result, a PI controller action is obtained, where the gain depends on the state of the system and is adapted instantaneously on-line. The algorithm is tested on a nonisothermal continuous stirred tank reactor (CSTR), considering both single-input single-output (SISO) and multi-input multi-output (MIMO) control problems. Simulation results show that the proposed controller provides satisfactory performance during set-point changes and disturbance rejection.

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