Abstract
A prediction control algorithm is presented based on least squares support vector machines (LS-SVM) model for a class of complex systems with strong nonlinearity. The nonlinear off-line model of the controlled plant is built by LS-SVM with radial basis function (RBF) kernel. In the process of system running, the off-line model is linearized at each sampling instant, and the generalized prediction control (GPC) algorithm is employed to implement the prediction control for the controlled plant. The obtained algorithm is applied to a boiler temperature control system with complicated nonlinearity and large time delay. The results of the experiment verify the effectiveness and merit of the algorithm.
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This work has been supported by the National Outstanding Youth Science Foundation of China (No. 60025308) and the Teach and Research Award Program for Outstanding Young Teachers in Higher Education Institutions of MOE, China.
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Liu, B., Su, H., Huang, W. et al. Temperature prediction control based on least squares support vector machines. J. Control Theory Appl. 2, 365–370 (2004). https://doi.org/10.1007/s11768-004-0041-7
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DOI: https://doi.org/10.1007/s11768-004-0041-7