A genetically trained adaptive neuro-fuzzy inference system network utilized as a proportional-integral-derivative-like feedback controller for non-linear systems

Abstract

This paper presents a genetically trained PID (proportional-integral-derivative)-like ANFIS (adaptive neuro-fuzzy inference system) acting as a feedback controller to control non-linear systems. Three important issues are addressed in this paper, which are, first, the evaluation of the ANFIS as a PID-like controller; second, the utilization of the GA (genetic algorithm) alone to train the ANFIS controller, instead of the hybrid learning methods that are widely used in the literature; and, third, the determination of the input and output scaling factors for this controller by the GA. The GA, with real-coding operators, is used to adjust all of the ANFIS parameters, which include the input and output scaling factors, the centres and widths of the input membership functions (MFs), and the consequent parameters.

To show the effectiveness of this controller and its learning method, several non-linear plants, including the CSTR (continuous stirred tank reactor), have been selected to be controlled by this controller through simulation. Moreover, this controller's robustness to output disturbances has also been tested and the results clearly indicated the remarkable performance of this controller and its learning algorithm. In addition, the result of comparing the performance of this controller with a genetically tuned classical PID controller has shown the superiority of the PID-like ANFIS controller.

Keywords

genetic algorithms ANFIS neuro-fuzzy systems classical PID controller

Get full access to this article

View all access options for this article.

References

Jang

J.-S. R.

‘ANFIS: Adaptive-network-based fuzzy inference system.’

IEEE Trans. on Systems, Man, and Cybernetics, May—June 1993, 23(3), 665–685.

Jang

J.-S. R.

Sun

C.-T.

Mizutani

Neuro-fuzzy and soft computing: a computational approach to learning and machine intelligence, (Prentice-Hall, Englewood Cliffs, New Jersey. 1997)

Turkmen

Guney

‘Genetic tracker with adaptive neuro-fuzzy inference system for multiple target tracking.’

Expert Systems with Applics 35 (4) (2008): 1657–1667

Jang

J.-S. R.

‘Self-learning fuzzy controllers based on temporal back propagation.’

IEEE Trans. on Neural Networks, September 1992, 3(5), 714–723.

Schirru

Oliveira

M. V.

‘Applying particle swarm optimization algorithm for tuning a neuro-fuzzy inference system for sensor monitoring.’

Prog. Nucl. Energy, May 2008, 1–7.

Shoorehdeli

M. A.

Teshnehlab

Sedigh

A. K.

‘A novel training algorithm in ANFIS Structure.’ In the American Control Conference, Minneapolis, Minnesota, 14–16 June 2006, pp. 5059–5064.

Shoorehdeli

M. A.

Teshnehlab

Sedigh

A. K.

‘Novel hybrid learning algorithms for tuning ANFIS parameters using adaptive weighted PSO.’ In IEEE International Fuzzy Systems Conference, FUZZ-IEEE 2007, Imperial College, London, 23–26 July 2007, pp. 1–6.

Ghomsheh

V. S.

Shoorehdeli

M. A.

Teshnehlab

‘Training ANFIS structure with modified PSO algorithm.’ In Mediterranean Conference on

Control and automation, Athens, Greece, 2007.

Lin

C.-J.

‘A GA-based neural fuzzy system for temperature control.’

Fuzzy Sets and Systems, April 2004, 143(2), 311–333.

10.

Zhao

‘A novel learning method for ANFIS using EM algorithm and emotional learning.’ In International Conference on

Computational intelligence and security, Harbin, China, 15–19 December 2007, pp. 23–27.

11.

Hímer

Dévényi

Kovács

Kortela

‘Fuzzy control of combustion with genetic learning automata.’ In Proceedings of the 16th European Simulation Symposium, 2004.

12.

Al-Said

I. A. M.

Genetic algorithms based intelligent control PhD Thesis, University of Technology, Baghdad, Iraq, January 2000.

13.

Mitchell

An introduction to genetic algorithms, (1998) 1st paperback edition, (MIT Press, Cambridge, Massachusetts).

14.

Narendra

K. S.

Parthasarathy

‘Identification and control of dynamical systems using neural networks.’

IEEE Trans. on Neural Networks, March 1990, 1(1), 4–27.

15.

Pham

D. T.

Xing

Neural networks for identification, prediction and control, 3rd printing, (Springer-Verlag, London 1997)

16.

Nahas

E. P.

Henson

M. A.

Seborg

D. E.

‘Nonlinear internal model control strategy for neural network models.’

Computers Chem. Engng 16 (12) (1992): 1039–1057

17.

Mikles

Fikar

Process modelling, identification, and control, (Springer-Verlag, Berlin, Heidelberg 2007)