Abstract
This study presents a novel hybrid classifier method to obtain the best parameters of a PID controller for desired specifications. The study presents a hybrid system based on the organization of existing rules and classifier models that select the optimal expressions to improve specifications. The model achieved chooses the best controller parameters among different closed loop tuning methods. The classifiers are based on ANN and SVM. The proposal was tested on the temperature control of a laboratory stove.
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References
Hsu, C., Chen, G., Lee, T.: Robust intelligent tracking control with PID-type learning algorithm. Neurocomputing 71, 234–243 (2007)
Gottwald, S.: Mathematical Fuzzy Control. A Survey of Some Recent Results. Logic Journal of IGPL 13, 525–541 (2005)
Zhang, J., Zhuang, J., Du, H., Wang, S.: Self-organizing genetic algorithm based tuning of PID controllers. Information Sciences 179, 1007–1018 (2009)
Juang, Y., Chang, Y., Huang, C.: Design of fuzzy PID controllers using modified triangular membership functions. Information Sciences 178, 1325–1333 (2008)
Liu, H., Coghill, G.: A model-based approach to robot fault diagnosis. Knowledge-Based Systems 18, 225–233 (2005)
Lu, H., Chang, J., Yeh, M.: Design and analysis of direct-action CMAC PID controller. Neurocomputing 70, 2615–2625 (2007)
Sala, A., Cuenca, Á., Salt, J.: A retunable PID multi-rate controller for a networked control system. Information Sciences 179, 2390–2402 (2009)
Sumar, R.R., Coelho, A.A.R., Coelho, L.D.S.: Computational intelligence approach to PID controller design using the universal model. Information Sciences 180, 3980–3991 (2010)
Thangaraj, R., Chelliah, T.R., Pant, M., Abraham, A., Grosan, C.: Optimal gain tuning of PI speed controller in induction motor drives using particle swarm optimization. Logic Journal of IGPL 19, 343–356 (2010)
Juang, Y., Chang, Y., Huang, C.: Design of fuzzy PID controllers using modified triangular membership functions. Information Sciences 178, 1325–1333 (2008)
Ye, J.: Adaptive control of nonlinear PID-based analog neural networks for a nonholonomic mobile robot. Neurocomputing 71, 1561–1565 (2008)
Romero, J.A., Sanchis, R., Balaguer, P.: PI and PID auto-tuning procedure based on simplified single parameter optimization. Journal of Process Control 21, 840–851 (2011)
Sun, J., Zhang, D., Li, X., Zhang, J., Du, D.: Smith Prediction Monitor AGC System Based on Fuzzy Self-Tuning PID Control. International Journal of Iron and Steel Research 17, 22–26 (2010)
Astrom, K.J., Hagglund, T.: Advanced PID Control. Pearson Education, Madrid (2009)
Feng, Y.L., Tan, K.C.: Process identification and PID Control. John Wiley & Sons, USA (2009)
Visioli, A.: Practical PID Control. Springer, London (2010)
Johnson, M.A., Moradi, M.H.: PID Control: New identification and Design methods. Springer, London (2010)
Seki, H., Shigemasa, T.: Retuning oscillatory PID control loops based on plant operation data. J. Process Control 20, 217–227 (2010)
Tyreus, B.D., Luyben, W.L.: Tuning PI controllers for integrator/dead time processes. Industrial Engineering Chemistry Research 11, 2625–2628 (1992)
Astrom, K.J., Hagglund, T.: Benchmark Systems for PID Control. In: Preprints FAC Workshop on Digital Control. Past, Present and Future of PID Control, Tarrasa, pp. 181–182 (2000)
Vapnic, V.: The nature of statistical learning theory. Springer, New York (1995)
Cristianini, N., Shawe-Taylor, J.: An Introduction to Support Vector Machines. Cambridge University Press, Cambridge (2000)
Parr, O.: Data Mining Cookbook. Modeling Data for Marketing, Risk, and Customer Relationship Management. John Wiley & Sons, Inc., New York (2001)
Duda, R.O., Hart, P.E., Stork, D.G.: Pattern Classification. John Wiley & Sons, Inc., Canada (2001)
Mitchell, T.M.: Machine Learning. McGraw-Hill, New York (1997)
Frank, E., Witten, I.: Data Mining: Practical Machine Learning Tools and Techniques, 2nd edn. Morgan Kaufmann (2005)
Rokach, L., Maimon, O.: Data Mining with Decision Trees: Theory and Applications. World Scientific Publishing, USA (2008)
Alpaydin, E.: Introduction to Machine Learning. The MIT Press, Oxford (2009)
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Calvo-Rolle, J.L., Corchado, E., Quintian-Pardo, H., García, R.F., Román, J.Á., Hernández, P.A. (2012). A Novel Hybrid Intelligent Classifier to Obtain the Controller Tuning Parameters for Temperature Control. In: Corchado, E., Snášel, V., Abraham, A., Woźniak, M., Graña, M., Cho, SB. (eds) Hybrid Artificial Intelligent Systems. HAIS 2012. Lecture Notes in Computer Science(), vol 7208. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-28942-2_61
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DOI: https://doi.org/10.1007/978-3-642-28942-2_61
Publisher Name: Springer, Berlin, Heidelberg
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