Abstract
Used for industrial process with different degree of nonlinearity, the two predictive control algorithms presented in this paper are based on Least Squares Support Vector Machines (LS-SVM) model. For the weakly nonlinear system, the system model is built by using LS-SVM with linear kernel function, and then the obtained linear LS-SVM model is transformed into linear input-output relation of the controlled system. However, for the strongly nonlinear system, the off-line model of the controlled system is built by using LS-SVM with Radial Basis Function (RBF) kernel. The obtained nonlinear LS-SVM model is linearized at each sampling instant of system running, after which the on-line linear input-output model of the system is built. Based on the obtained linear input-output model, the Generalized Predictive Control (GPC) algorithm is employed to implement predictive control for the controlled plant in both algorithms. The simulation results after the presented algorithms were implemented in two different industrial processes model; respectively revealed the effectiveness and merit of both algorithms.
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Project supported by the National Outstanding Youth Science Foundation of China (No. 60025308) and the Teach and Rescarch Award Program for Outstanding Young Teachers in Higher Education Institutions of MOE, China
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Bin, L., Hong-ye, S. & Jian, C. New predictive control algorithms based on Least Squares Support Vector Machines. J. Zheijang Univ.-Sci. A 6, 440–446 (2005). https://doi.org/10.1631/jzus.2005.A0440
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DOI: https://doi.org/10.1631/jzus.2005.A0440
Key words
- Least Squares Support Vector Machines
- Linear kernel function
- RBF kernel function
- Generalized predictive control