Abstract
In this paper, the effects of using multi RBF kernel for an online LSSVR on modeling and control performance are investigated. The Jacobian information of the system is estimated via online LSSVR model. Kernel parameter determines how the measured input is mapped to the feature space and a better plant model can be achieved by discarding redundant features. Therefore, introducing flexibility in kernel function helps to determine the optimal kernel. In order to interfuse more flexibility to the kernel, linear combinations of RBF kernels have been utilized. The purpose of this paper is to improve the modeling performance of the LSSVR and also control performance obtained by adaptive PID by tuning bandwidths of the RBF kernels. The proposed method has been evaluated by simulations carried out on a continuously stirred tank reactor (CSTR), and the results show that there is an improvement both in modeling and control performances.
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References
Choi, Y., Chung, W.K.: PID Trajectory Tracking Control for Mechanical Systems. LNCIS. Springer, Berlin (2004)
Ucak, K., Oke, G.: Adaptive PID Controller Based on Online LSSVR with Kernel Tuning. In: International Symposium on Innovations in Intelligent Systems and Applications, Istanbul, Turkey (2011)
Wanfeng, S., Shengdun, Z., Yajing, S.: Adaptive PID Controller Based on Online LSSVM Identification. In: IEEE/ASME International Conference on Advanced Intelligent Mechatronics, vol. 1-3, pp. 694–698 (2008)
Zhao, J., Li, P., Wang, X.S.: Intelligent PID Controller Design with Adaptive Criterion Adjustment via Least Squares Support Vector Machine. In: 21st Chinese Control and Decision Conference (2009)
Takao, K., Yamamoto, T., Hinamoto, T.: A design of PID controllers with a switching structure by a support vector machine. In: International Joint Conference on Neural Network, Vancouver, BC, Canada (2006)
Iplikci, S.: A comparative study on a novel model-based PID tuning and control mechanism for nonlinear systems. International Journal of Robust and Nonlinear Control 20, 1483–1501 (2010)
Campbell, W.M., Sturim, D.E., Reynolds, D.A.: Support vector machines using GMM supervectors for speaker verification. IEEE Signal Processing Letters 13(5) (2006)
Iplikci, S.: Controlling the Experimental Three-Tank System via Support Vector Machines. In: Kolehmainen, M., Toivanen, P., Beliczynski, B. (eds.) Adaptive and Natural Computing Algorithms. LNCS, vol. 5495, pp. 391–400. Springer, Heidelberg (2009)
Guo, Y.C.: An Integrated PSO for Parameter Determination and feature selection of SVR and its application in STLF. In: International Conference on Machine Learning and Cybernetics, Baoding, PR China, pp. 359–364 (2009)
Momma, M., Bennett, K.P.: A pattern search method for model selection of support vector regression. In: International Conference on Data Mining, Arlington VA, pp. 261–274 (2002)
Hou, L.K., Yang, Q.X.: Study on parameters selection of LSSVR based on Grid-Diamond search method. In: International Conference on Machine Learning and Cybernetics, Baoding, PR China, vol. 1-6, pp. 1219–1224 (2009)
Farag, A., Mohamed, R.M.: Regression using support vector machines: basic foundations, Technical Report (2004)
Suykens, J.A.K.: Nonlinear modeling and support vector machines. In: IEEE Instrumentation and Measurement Technology Conference Budapest, Hungary (2001)
Gunn, S.: Support Vector Machines for Classification and Regression, ISIS Technical Report (1998)
Smola, A.J., Scholkopf, B.: A tutorial on support vector regression. Statistics and Computing 14, 199–222 (2004)
Zhu, Y.F., Mao, Z.Y.: Online Optimal Modeling of LS-SVM based on Time Window. In: IEEE International Conference on Industrial Technology, Hammamet, Tunisia, vol. 1-3, pp. 1325–1330 (2004)
Bobal, V., Böhm, J., Fessl, J., Macháček, L.: Digital-Self Tuning Controllers, Advanced Textbooks in Control and Signal Processing. Springer, London (2005)
Luenberger, D.G., Ye, Y.: Linear and Nonlinear Programming, 3rd edn. Springer Science + Business Media, LLC (2008)
Wu, W., Chou, Y.S.: Adaptive feedforward and feedback control of non-linear time-varying uncertain systems. International Journal of Control 72, 1127–1138 (1999)
Ungar, L.H.: A bioreactor benchmark for adaptive-network based process control. In: Neural Network for Control. MIT Press, Cambridge (1990)
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Ucak, K., Oke, G. (2011). An Improved Adaptive PID Controller Based on Online LSSVR with Multi RBF Kernel Tuning. In: Bouchachia, A. (eds) Adaptive and Intelligent Systems. ICAIS 2011. Lecture Notes in Computer Science(), vol 6943. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-23857-4_8
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DOI: https://doi.org/10.1007/978-3-642-23857-4_8
Publisher Name: Springer, Berlin, Heidelberg
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