Auto Tuning PID Controller based on Improved Genetic Algorithm for Reverse Osmosis Plant
An optimal control of Reverse Osmosis (RO) plant is
studied in this paper utilizing the auto tuning concept in conjunction
with PID controller. A control scheme composing an auto tuning
stochastic technique based on an improved Genetic Algorithm (GA) is
proposed. For better evaluation of the process in GA, objective
function defined newly in sense of root mean square error has been
used. Also in order to achieve better performance of GA, more
pureness and longer period of random number generation in operation
are sought. The main improvement is made by replacing the uniform
distribution random number generator in conventional GA technique
to newly designed hybrid random generator composed of Cauchy
distribution and linear congruential generator, which provides
independent and different random numbers at each individual steps in
Genetic operation. The performance of newly proposed GA tuned
controller is compared with those of conventional ones via simulation.
[1] UNESCO (2004, may 11). Water for - water for life - the united nations
world water development report. [Online]. Available:
http://www.unesco.org/water/wwap/wwdr/ex_summary/
[2] M.S. Mohsen, 0.R AI-Jayyousi, "Brackish water desalination: an
alternative for water supply enhancement in Jordan--, Desalination,
vol.124, p~163-174, Nov.1999
[3] A. Maurel, "Desalination of sea Water and brackish water," in Proc. Of
seminar on water managemen6 strategies in Mediterranean countries
[4] F. G. Shinskey, Process Control System: Application, Design and Tuning.
McGraw-Hill, 4th ed, 1996
[5] K. J. Astrom and B. Wittenmark, Adaptive Control. Addison Wesley, 2nd
ed., 1995.
[6] A. Visioli, "Tuning of PID controllers with fuzzy logic," Proc. Inst. Elect.
Eng. Contr. Theory Applicat., vol. 148, no. 1, pp. 1-8, Jan. 2001.
[7] R. A. Krohling and J. P. Rey, "Design of optimal disturbance rejection
PID controllers using genetic algorithm," IEEE Trans. Evol. Comput., vol.
5, pp. 78-82, Feb. 2001.
[8] Zwe-Lee Gaing, "A Particle Swarm Optimization Approach for Optimum
Design of PID Controller in AVR System," IEEE TRANSACTIONS ON
ENERGY CONVERSION, VOL. 19, NO. 2, pp.384~391, 2004.
[9] Dionisio S. Pereira, "Genetic Algorithm Based System Identification and
PID Tuning for Optimum Adaptive Control," International Conference
on Advanced Intelligent Mechatronics, Monterey, California, USA, 24-28
July, pp.801~806, 2005
[10] Ian Griffin, "On-line PID Controller Tuning using Genetic Algorithms,"
Dublin City University, 2003
[11] T O.Mahony, C J Downing and K Fatla, "Genetic Algorithm for PID
Parameter Optimization: Minimizing Error Criteria," Process Control and
Instrumentation 2000 26-28 July 2000, University of Stracthclyde,
pp.148~153
[12] C. R. Houck, J. Joines. and M.Kay, "A genetic algorithm for function
optimization: A Matlab implementation," ACM Transactions on
Mathematical Software, 1996.
[13] Chipperfield, A. J., Fleming, P. J., Pohlheim, H. and Fonseca, C. M., A
"Genetic Algorithm Toolbox for MATLAB," Proc. International
Conference on Systems Engineering, Coventry, UK, 6-8 September, 1994
[14] D. E. Goldberg, "Genetic Algorithms in Search, Optimization, and
Machine Learning," Addison-Wesley Publishing Co., Inc., 1989
[15] Wikipedia, http://en.wikipedia.org/wiki/Cauchy_distribution
[16] Stephen K. Park and Keith W. Miller. Random number generators: Good
ones are hard to find. CACM, 31(10):1192-1201, 1988.
[17] Donald E. Knuth. "Deciphering a linear congruential encryption,". IEEE
Transactions on Information Theory, IT-31(1):49-52, January 1985.
[18] T. K. Teng, J. S. Shieh and C. S. Chen, "Genetic algorithms applied in
online autotuning PID parameters of a liquid-level control system,"
Transaction of the Institute of Measurement and control 25, 5 (2003),
pp.433~450
[1] UNESCO (2004, may 11). Water for - water for life - the united nations
world water development report. [Online]. Available:
http://www.unesco.org/water/wwap/wwdr/ex_summary/
[2] M.S. Mohsen, 0.R AI-Jayyousi, "Brackish water desalination: an
alternative for water supply enhancement in Jordan--, Desalination,
vol.124, p~163-174, Nov.1999
[3] A. Maurel, "Desalination of sea Water and brackish water," in Proc. Of
seminar on water managemen6 strategies in Mediterranean countries
[4] F. G. Shinskey, Process Control System: Application, Design and Tuning.
McGraw-Hill, 4th ed, 1996
[5] K. J. Astrom and B. Wittenmark, Adaptive Control. Addison Wesley, 2nd
ed., 1995.
[6] A. Visioli, "Tuning of PID controllers with fuzzy logic," Proc. Inst. Elect.
Eng. Contr. Theory Applicat., vol. 148, no. 1, pp. 1-8, Jan. 2001.
[7] R. A. Krohling and J. P. Rey, "Design of optimal disturbance rejection
PID controllers using genetic algorithm," IEEE Trans. Evol. Comput., vol.
5, pp. 78-82, Feb. 2001.
[8] Zwe-Lee Gaing, "A Particle Swarm Optimization Approach for Optimum
Design of PID Controller in AVR System," IEEE TRANSACTIONS ON
ENERGY CONVERSION, VOL. 19, NO. 2, pp.384~391, 2004.
[9] Dionisio S. Pereira, "Genetic Algorithm Based System Identification and
PID Tuning for Optimum Adaptive Control," International Conference
on Advanced Intelligent Mechatronics, Monterey, California, USA, 24-28
July, pp.801~806, 2005
[10] Ian Griffin, "On-line PID Controller Tuning using Genetic Algorithms,"
Dublin City University, 2003
[11] T O.Mahony, C J Downing and K Fatla, "Genetic Algorithm for PID
Parameter Optimization: Minimizing Error Criteria," Process Control and
Instrumentation 2000 26-28 July 2000, University of Stracthclyde,
pp.148~153
[12] C. R. Houck, J. Joines. and M.Kay, "A genetic algorithm for function
optimization: A Matlab implementation," ACM Transactions on
Mathematical Software, 1996.
[13] Chipperfield, A. J., Fleming, P. J., Pohlheim, H. and Fonseca, C. M., A
"Genetic Algorithm Toolbox for MATLAB," Proc. International
Conference on Systems Engineering, Coventry, UK, 6-8 September, 1994
[14] D. E. Goldberg, "Genetic Algorithms in Search, Optimization, and
Machine Learning," Addison-Wesley Publishing Co., Inc., 1989
[15] Wikipedia, http://en.wikipedia.org/wiki/Cauchy_distribution
[16] Stephen K. Park and Keith W. Miller. Random number generators: Good
ones are hard to find. CACM, 31(10):1192-1201, 1988.
[17] Donald E. Knuth. "Deciphering a linear congruential encryption,". IEEE
Transactions on Information Theory, IT-31(1):49-52, January 1985.
[18] T. K. Teng, J. S. Shieh and C. S. Chen, "Genetic algorithms applied in
online autotuning PID parameters of a liquid-level control system,"
Transaction of the Institute of Measurement and control 25, 5 (2003),
pp.433~450
@article{"International Journal of Information, Control and Computer Sciences:60060", author = "Jin-Sung Kim and Jin-Hwan Kim and Ji-Mo Park and Sung-Man Park and Won-Yong Choe and Hoon Heo", title = "Auto Tuning PID Controller based on Improved Genetic Algorithm for Reverse Osmosis Plant", abstract = "An optimal control of Reverse Osmosis (RO) plant is
studied in this paper utilizing the auto tuning concept in conjunction
with PID controller. A control scheme composing an auto tuning
stochastic technique based on an improved Genetic Algorithm (GA) is
proposed. For better evaluation of the process in GA, objective
function defined newly in sense of root mean square error has been
used. Also in order to achieve better performance of GA, more
pureness and longer period of random number generation in operation
are sought. The main improvement is made by replacing the uniform
distribution random number generator in conventional GA technique
to newly designed hybrid random generator composed of Cauchy
distribution and linear congruential generator, which provides
independent and different random numbers at each individual steps in
Genetic operation. The performance of newly proposed GA tuned
controller is compared with those of conventional ones via simulation.", keywords = "Genetic Algorithm, Auto tuning, Hybrid random
number generator, Reverse Osmosis, PID controller", volume = "2", number = "11", pages = "3891-6", }
