Multiple Model and Neural based Adaptive Multi-loop PID Controller for a CSTR Process
Multi-loop (De-centralized) Proportional-Integral-
Derivative (PID) controllers have been used extensively in process
industries due to their simple structure for control of multivariable
processes. The objective of this work is to design multiple-model
adaptive multi-loop PID strategy (Multiple Model Adaptive-PID)
and neural network based multi-loop PID strategy (Neural Net
Adaptive-PID) for the control of multivariable system. The first
method combines the output of multiple linear PID controllers,
each describing process dynamics at a specific level of operation.
The global output is an interpolation of the individual multi-loop
PID controller outputs weighted based on the current value of the
measured process variable. In the second method, neural network
is used to calculate the PID controller parameters based on the
scheduling variable that corresponds to major shift in the process
dynamics. The proposed control schemes are simple in structure with
less computational complexity. The effectiveness of the proposed
control schemes have been demonstrated on the CSTR process,
which exhibits dynamic non-linearity.
[1] M. Morari and E. Zafiriou, Robust Process Control, Upper Saddle River,
NJ: Prentice Hall, 1989.
[2] B. Wayne Bequette, Process Control, Modeling, Design and Simulation,
India, Prentice Hall, 2004.
[3] M. Pottmann and D.E. Seborg, Identification of Non-linear Process Using
Reciprocal Multi quadratic Functions, Journal of Process Control, vol
2, pp.189-203, 1992.
[4] M. Jalili Kharaajoo, Predictive Control of a Continuous Stirred Tank
Reactor based on Neuro-fuzzy Model of the Process, SICE Annual
Conference in Fukui, vol 57, pp.3005-3011, Aug 2003.
[5] Venugopal G. Krishnapura and Arthur Jutan, A Neural Adaptive Controller,
Chemical Engineering Science, vol 55, pp.3803-3812, 2000.
[6] D. Danielle and D. Cooper, A Practical Multiple Model Adaptive Strategy
for Multivariable Model Predictive Control, Control Engineering
Practice, vol 11, pp.649-664, 2003.
[1] M. Morari and E. Zafiriou, Robust Process Control, Upper Saddle River,
NJ: Prentice Hall, 1989.
[2] B. Wayne Bequette, Process Control, Modeling, Design and Simulation,
India, Prentice Hall, 2004.
[3] M. Pottmann and D.E. Seborg, Identification of Non-linear Process Using
Reciprocal Multi quadratic Functions, Journal of Process Control, vol
2, pp.189-203, 1992.
[4] M. Jalili Kharaajoo, Predictive Control of a Continuous Stirred Tank
Reactor based on Neuro-fuzzy Model of the Process, SICE Annual
Conference in Fukui, vol 57, pp.3005-3011, Aug 2003.
[5] Venugopal G. Krishnapura and Arthur Jutan, A Neural Adaptive Controller,
Chemical Engineering Science, vol 55, pp.3803-3812, 2000.
[6] D. Danielle and D. Cooper, A Practical Multiple Model Adaptive Strategy
for Multivariable Model Predictive Control, Control Engineering
Practice, vol 11, pp.649-664, 2003.
@article{"International Journal of Information, Control and Computer Sciences:52459", author = "R.Vinodha S. Abraham Lincoln and J. Prakash", title = "Multiple Model and Neural based Adaptive Multi-loop PID Controller for a CSTR Process", abstract = "Multi-loop (De-centralized) Proportional-Integral-
Derivative (PID) controllers have been used extensively in process
industries due to their simple structure for control of multivariable
processes. The objective of this work is to design multiple-model
adaptive multi-loop PID strategy (Multiple Model Adaptive-PID)
and neural network based multi-loop PID strategy (Neural Net
Adaptive-PID) for the control of multivariable system. The first
method combines the output of multiple linear PID controllers,
each describing process dynamics at a specific level of operation.
The global output is an interpolation of the individual multi-loop
PID controller outputs weighted based on the current value of the
measured process variable. In the second method, neural network
is used to calculate the PID controller parameters based on the
scheduling variable that corresponds to major shift in the process
dynamics. The proposed control schemes are simple in structure with
less computational complexity. The effectiveness of the proposed
control schemes have been demonstrated on the CSTR process,
which exhibits dynamic non-linearity.", keywords = "Multiple-model Adaptive PID controller, Multivariableprocess, CSTR process.", volume = "4", number = "8", pages = "1216-6", }