Commenced in January 2007
Frequency: Monthly
Edition: International
Paper Count: 30835
Multiple Model and Neural based Adaptive Multi-loop PID Controller for a CSTR Process

Authors: R.Vinodha S. Abraham Lincoln, J. Prakash


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

Digital Object Identifier (DOI):

Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 1733


[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.