Decoupling Fuzzy-Neural PID Controller for TITO Nonlinear Systems

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This paper presents a fuzzy-neural structure of a Decoupling Fuzzy PID controller with self-tuning parameters. This structure is appropriate for Two-Input-Two-Output (TITO) nonlinear system. The main advantage here is that the equation of classical PID control and decoupling coefficients are used as a Sugeno function into the fuzzy rules. Hence the designed decoupling fuzzy PID controller can be viewed as a natural similarity to the conventional one with decoupling elements. A benchmark quadruple tank, implementing a TITO nonlinear system is considered to illustrate the benefits of the design paradigm. The performance of this set up was studied for reference tracking and disturbance rejection cases. Simulation results confirm the effectiveness of the proposed solution.

1. Astrom, K. and B. Witenmark. Computer Controlled Systems. 3 Ed., Prentice Hall, 1997.

2. Digital Control of a Quadruple-Tank Process. ELE/MCE 503 Final Project, Fall 2012.

3. Glattfelder, A. H., W. Schaufelberger. Control Systems with Input and Output Constraints. Springer, 2003.

4. Gomi, H. and M. Kawato. Neural Network Control for a Closed-Loop System Using Feedback-Error-Learning. – Neural Networks, 6, 1993.

5. Takagi, H. and M. Sugeno. Fuzzy Identification of Systems and its Applications to Modeling and Control. – IEEE Trans. on Systems, Man., and Cybern., SMC-15(1), 1985, 116-132.

6. Petrov, M., I. Ganchev, A. Taneva. Fuzzy PID Control of Nonlinear Plants. Proceedings of the IEEE International Symposium on “Intelligent Systems”, Varna, Bulgaria, 10-13 September 2002, IEEE Catalogue Number 02EX499, ISBN 0-7803-7601-3, 1, 30-35.

7. Ahmed, S., I. Ganchev, A. Taneva. M. Petrov. Decoupling Neuro-Fuzzy Model Predictive Controllers Applied to Quadruple Tanks. 2016 IEEE 8th International Conference on Intelligent Systems, 610-615.

8. Chia-Feng, Juang and Chin-Teng Lin. An On-Line Self-Constructing Neural Fuzzy Inference Network and Its Applications. – IEEE trans. Fuzzy System, 6, 1999, No. 2, 13-31.

9. Yang, J. R. ANFIS: Adaptive-Network-Based Fuzzy Inference System. – IEEE Transactions on Systems, Man, and Cybernetic, 23, May/June 1993, No. 3, 665-685.

10. Nurnberger, A., D. Nauck and R Kruse. Neuro-fuzzy Control Based on the Nefcon-model: Recent Developments. – Soft Computing, 2, 1999, 168-182.

11. Azeem, Mohammad Fazle, Madasu Hanmandlu, Nesar Ahmed. Structure Identification of Generalized Adaptive Neuro-Fuzzy Inference Systems. – IEEE Trans. Fuzzy System., 11, Oct. 2003, No. 5, 666-681.

12. Shie-Jue, Lee and Chen-Sen Ouyang. A Neuro-fuzzy System Modeling with Self-Constructing Rule Generation and Hybrid SVD-Based Learning. – IEEE Trans. Fuzzy System., 11, June 2003, No. 3, 341-353.

13. Chin-Teng Lin and C. S. George Lee. Reinforcement Structure/Parameter Learning for Neural Network Based Fuzzy Logic Control Systems. – IEEE Trans. Fuzzy System., 2, February 1994, No. 1, 46-73.

14. Castellano, G. and A. M. Fanelli. Fuzzy Inference and Rule Extraction and Using Neural Network. – Neural Network World Journal, 3, 2000, 361-371.

15. Chin-Teng Lin and Ya-Ching Lu. A Neural Fuzzy System with Fuzzy Supervised Learning. – IEEE Transactions on Systems, Man, and Cybernetic-Part B, 26, Oct. 1996, No. 5, 744-763.

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